2011
Sebastiaan Lommelaars
The promise of personalized medicine
Review of the possibilities and hurdles to achieve a cost-effective
personalized healthcare system.
Supervisor: Prof. Dr. R. Bernards, PhD
Molecular Carcinogenesis
NKI-AVL
Abstract
Until now we have approached cancer treatment from a histological point of view, fighting tumors
with indifferent cytotoxic drugs. Personalized medicine promises a much needed change to this outdated treatment approach. Here I review both the promises and obstacles of targeted therapeutics’
development. Extra attention is put on the financial viability of personalized medicine. By modeling
the development costs and revenue possibilities I describe how this approach can be cost-effective to
pharmaceutical companies and other parties involved. The individualization of cancer treatment will
greatly improve prognosis saving patients the detrimental effects of systemic cytotoxic treatment
and relieving some pressure of the great socio-economic burden that cancer puts on our society.
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Contents
Abstract ................................................................................................................................................... 2
1. Introduction ......................................................................................................................................... 4
1.1 Conventional cancer therapy ........................................................................................................ 4
1.2 The promise of personalized medicine ......................................................................................... 4
1.3 Cancer biomarkers ........................................................................................................................ 5
1.4 Biomarker discovery ...................................................................................................................... 6
2. Obstacles to personalized medicine.................................................................................................... 7
2.1 Technical difficulties with personalized medicine......................................................................... 7
2.1 Getting suitable tumor tissue ........................................................................................................ 8
CASE: Agendia, market entry of a genetic test .......................................................................... 9
2.3 Regulatory clarity ........................................................................................................................ 12
2.4 Getting the right mindset ............................................................................................................ 14
3. Viability of personalized medicine .................................................................................................... 16
3.1 Adaptive clinical trials.................................................................................................................. 16
3.2 Drug development costs ............................................................................................................. 19
3.3 Economic modeling of R&D productivity .................................................................................... 20
CASE: Vemurafenib, small Phase III clinical trial design ........................................................ 20
3.4 Calculating the potential cost reductions of personalized medicine .......................................... 25
CASE: Iressa, Tarceva , Herceptin and the value of a biomarker. ........................................ 26
3.5 Revenues generated by personalized medicine.......................................................................... 36
4. Discussion .......................................................................................................................................... 38
References ............................................................................................................................................. 40
Glossary ................................................................................................................................................. 44
Appendices ............................................................................................................................................ 45
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1. Introduction
1.1 Conventional cancer therapy
An estimated 10^16 cell divisions take place in a normal human body over the course of a lifetime,
with mutations occurring about 10^-6 times per gene per cell division. This implicates that, even in
the absence of external mutagens such as tobacco smoke or X-rays, every single gene is likely to
undergo mutation on a staggering 10^10 separate occasions. Fortunately cells employ complex
interconnected regulatory mechanisms to help them maintain precise control of the integrity of their
genome. These need to be disrupted before a cell can give rise to a malignant tumor. Many
oncogenic driver mutations alter components of signaling pathways that cause proliferative signals,
switching on cell growth, regulate survival, DNA replication, and cell division in the tumor. Common
examples are mutations in receptor tyrosine kinases, such as the Epidermal Growth Factor Receptor
(EGFR), proteins of the downstream RAS family or in p53, a well known tumor suppressor protein.
Other signaling pathways act to inhibit proliferation, the best known example being the TGFβ
signaling pathway. During the progression of a tumor, cancer cells will constantly meet new barriers
to further expansion. For example when the tumor reaches a size of one or two millimeters nutrients
and oxygen supply will become a limiting factor to further growth. Each new barrier, whether
physical or physiological, must be overcome by the random acquisition of additional mutations. The
ways in which a gene can be mutated are enormously varied (point mutations, translocations and
copy number gains or losses). Any genetic accident that can increase, decrease, or change the activity
of a signaling pathway is likely to be found somewhere in the increasingly complex catalogue of
genetic changes that occur in cancer (Alberts et al., 2002) This highly diverse roadway to malignancy
generates an immensely heterogeneous pool of tumors, not only in terms of histology and clinical
outcome, but also on a molecular level. As our understanding of these molecular processes
increases, we are forced to come to our senses and realize there will be no single cure for cancer,
ever. We need to find ways to categorize patient populations and treat their individual tumor types
with high accuracy.
1.2 The promise of personalized medicine
Until now we have approached cancer treatment from a histological point of view, classifying tumors
based on histopathological indications and fighting them with indifferent cytotoxic drugs.
Chemotherapy ignores the deeper molecular reasons that lie at the core of tumor progression and
destroys any rapidly replicating cell in the body, including healthy cells. The processes underlying
tumor progression, will also allow the tumor to rapidly adjust to therapeutic agents, creating multiresistant tumors that are no longer affected by conventional therapies. The alternating oncogenic
driver mutations make that seemingly similar tumors will respond differently to therapeutic
interventions. These struggles have made it painfully clear that we need to move towards more
specific therapeutics to treat patients at an individual level. We are now entering the first and most
important stage of personalized medicine, where we will aim to group patients into smaller subpopulations, rationally predicting their response to newly designed targeted therapies. The
individualization of cancer treatment will greatly improve prognosis for patients as treatments will
prove more effective and spare patients from the current ‘trial and error’ approach of treatment
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selection, saving vital time and suffering. As cancer treatment puts an ever growing financial burden
on our society, the ability to rationally predict treatment response will reduce the number of patients
receiving expensive drugs without any clinical benefit. This socioeconomic aspect is expected to be a
strong driver for patients, pharmaceutical companies and regulatory bodies to move towards
personalized medicine.
1.3 Cancer biomarkers
Despite tumor heterogeneity, it is possible to classify tumors based on the activity of key
components of the major carcinogenic pathways. At this stage it would be far too laborious to
allocate each malignant mutation individually. As we are just beginning to understand the processes
underlying tumor growth it will take a long time before we know the precise roles of each abnormal
gene in these major pathways. With our current knowledge we are able to predict therapy response
based on key components of cancer-associated pathways. To detect this abnormal genomic activity
and predict treatment outcome it is vital that we look for new sets of cancer biomarkers, a
development that is already underway. For example, Epidermal Growth Factor Receptor (EGFR) gene
mutations and increased EGFR copy number have been associated with favorable response to EGFR
tyrosine kinase inhibitors (EGFR-TKI) like Tarceva and Iressa, in patients with non-small-cell lung
cancer (NSCLC) (Liang et al., 2010; Massarelli et al., 2007). It is thought that the competitive binding
of Tarceva and Iressa with the EGF receptor inhibits proliferative downstream signaling. With this
relatively global insight in the EGF pathway we are able to improve survival for most NSCLC patients
that carry activating EGFR
mutations (Figure 1 AB). This
demonstrates
that
‘pathway
activation biomarkers’ do not
require a complete understanding
of the pathway. However a
complicating factor in this
approach is that pathway
activation biomarkers usually
cannot
distinguish
between
upstream
or
downstream
activation of the pathway. When
the
pathway
is
activated
downstream of the targeted
intervention point, the drug cannot Figure 1 Pathway activation and inhibition. A: Activating mutations in the EGF
pathway result in proliferative signals to the cell. These signals in term fuel the
inhibit the proliferative signals of tumors malignant growth. B: TKIs inhibit EGFR from transmitting its proliferative
that pathway. For example signals, hereby reducing tumor progression C: When the EGF pathway is activated by
downstream components such as KRAS, PI3K or BRAF, TKIs cannot inhibit the
mutations in KRAS, PI3K, and BRAF, proliferative signals, so tumor growth continues unaffected by the treatment.
downstream effectors of EGF, have
been shown to predict poor response to TKIs and are even frequently mutated in the event of
acquired resistance to these targeted therapies (Figure 1 C) (Nguyen et al., 2009 ; Ludovini et al.,
2011; Schmid et al., 2009; Sunaga et al., 2011). This illustrates the need for strong biomarkers to
support proper use of targeted therapies. As our healthcare system moves towards more
personalized medicine the role of biomarkers will increase accordingly.
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Four different types of biomarkers can be distinguished.
1. Diagnostic biomarkers help with tumor classification and most importantly can be used for
early detection of tumor growth. This type of biomarker is less relevant to the development
of targeted therapies as it is used before treatment.
2. Prognostic biomarkers indicate if patients need additional therapy after surgery of the
primary tumor. In case of very aggressive tumor tissue the physician might decide to give
adjuvant treatment to reduce the chance of tumor recurrence.
3. Predictive biomarkers will then aide the physician in the choice between different targeted
therapies, predicting the patient’s response to the drugs. This is the most important class of
biomarkers as targeted therapies usually come with a companion biomarker of this kind.
4. Pharmacodynamic biomarkers help in the last step of treatment choice, indicating to the
physician the optimal dose for every individual patient.
1.4 Biomarker discovery
The key components of growth promoting pathways are vital for the survival of the tumor. Inhibiting
the upregulated EGFR growth signal with TKIs in patients with NSCLC frequently results in cancer cell
death. In fact this phenomenon of cancer cell death after sudden inhibition of growth promoting
signals is common amongst many types of cancer and is called “oncogene- or network addiction“
(Tonon, 2008; Weinstein et al., 2008; Sharma et al., 2010). This makes the key-components very
suitable targets for targeted therapies and thus valuable predictive biomarkers. Thanks to rapid
developments of new genomic technologies,
cancer genomes can now be analyzed at
multiple levels (Figure 2). Large-scale
functional genetic screens and genome-wide
RNA interference are used for identification
of cancer associated genes and DNA
microarray analysis is used to follow gene
expression on a high-throughput genomewide scale. Individual mutations in the
cancer genome can be found using largescale DNA sequence analysis whilst larger
copy number gains and losses can be
detected using comparative genomic
hybridization
(CGH).
Finally,
protein
biomarkers can be identified using tissue
micro arrays (TMAs) and reverse phase
protein lysate arrays (RPPAs). Relations
between oncogenes and the course of the Figure 2 Methods of biomarker discovery. There are a number of
disease have already been found using these promising ways for biomarker discovery, which are increasingly proving
their importance for this aspect of personalized medicine.
genome analysis techniques, ultimately
yielding promising biomarkers (Majewski et
al., 2011).
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2. Obstacles to personalized medicine
As the advantages of personalized
medicine become clearer and supporting
techniques more common one might
wonder why so few targeted therapeutics
are used today. There are a number of
obstacles to their development that have
not been resolved and some of them will
prove difficult, even in the near future.
One might say we are opening Pandora’s
Box with our search for targeted therapies.
By fighting cancer at a more specific level it
turns out that our knowledge of the driving
forces of malignancy is not always
sufficient. So before we can truly start
performing personalized medicine a Figure 3 Obstacles to personalized medicine. These obstacles will be
number of obstacles need to be overcome discussed in this chapter.
(Figure 3).
2.1 Technical difficulties with personalized medicine
By classifying and treating tumors based on pathway activation we overlook the specific genes and
proteins that might play a different role in individual tumors. Upstream or downstream pathway
activation is an example we discussed before.
A. Acquired drug resistance. Another problem with the pathway based approach is the fact that
tumors frequently acquire resistance to targeted therapies. With EGFR TKIs like Gefitinib and
Erlotinib we target the EGF pathway at the receptor, effectively knocking down the
malignant EGF signal in patients with growth promoting mutations in EGFR. However the life
expectancy of these patients is still poor as many acquire resistance to the therapy at a later
stage (Nguyen et al., 2009). Inhibitory feedback systems that normally stop DNA replication
in the event of serious mutations are lost well before the tumor is likely to be diagnosed. By
losing these and other proofreading mechanisms the tumor becomes genetically instable and
highly prone to mutations. Mutations that bypass our point of intervention at the EGF
receptor or that occur downstream in the pathway will restore the malignant downstream
EGF signal and allow for further tumor growth.
B. Primary and secondary tumor genome. Another property of mutation prone tumors is the
fact that metastasis in a single patient are likely to continue mutating on their own.
Consequently not all metastasis will respond to the same targeted therapy and it would be
necessary to diagnose each individual growth promoting mutation. It would be impossible to
obtain biopsies from every metastasis so for this approach to work we will need to rely on
non-invasive sampling techniques (Majewski et al., 2011). Ultimately this will help physicians
determining a cocktail of targeted therapeutics that are combined to destroy all tumor tissue
as effective as possible. These technical difficulties will be overcome as we gain more
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experience with targeted therapeutics over time. So for now we should focus our research
effort on targeted therapies and their accompanying biomarkers.
2.1 Getting suitable tumor tissue
Because biomarkers are such a vital aspect of targeted therapies, their discovery is one of the main
goals for research. As we noted before, biomarkers are typically discovered by various ways of tumor
tissue analysis. It’s this tissue’s availability that currently forms a bottleneck for swift discovery of
novel biomarkers.
A. Tissue preservation. After collection in the clinic, most tumor tissue is preserved through
Formalin Fixation and Paraffin Embedding (FFPE). This technique is well suited for
conventional histopathological assessments as it preserves tissue architecture and allows for
easy storage of surplus samples in banks. FFPE, as a clinical standard, has generated a vast
repository of tissue material for long-term clinical studies, but unfortunately it induces
chemical changes and degradation to the DNA, RNA and proteins, making the samples
suboptimal for biomarker research. The preservation issue has raised voices to introduce
fresh tissue preservation as a new clinical standard in molecular pathology which would be
better suited for biomarker discovery, but this comes at a price. The US based National
Cancer Institute has launched a campaign to promote high quality biospecimen collection
and banking for research purposes. In their guides they outline the operational, technical,
ethical, legal and policy best practices for tissue repositories. Nondiscrimination legislation
such as the US Genetic Information Nondiscrimination Act of 2008 (GINA) will also help by
encouraging people to donate specimens by ensuring patients their genetic information
cannot be misused by any company such as their employer or insurance. The global
introduction of new processing and preservation methods require a tremendous effort and
moreover the logistics of fresh tissue preservation will be more complex, costly and time
consuming. For now it’s worth having a closer look at FFPE samples and recently several
advances have been made to overcome the degrading effects. (Nirmalan et al., 2008; Berg et
al., 2010; Ralton et al., 2011) This makes the use of FFPE tissue for biomarker research
superior to the use of fresh tissue for reasons such as cost, availability, standardized
technique, easy long-term storage and most importantly it enables retrospective studies,
thus vastly increasing development speed.
B. Obtaining tumor tissue. A second tissue related problem is the fact that it is not easy to
obtain tumor tissue from patients. Most patients enroll clinical trials at a later stage of their
disease when the tumor has already spread to different sites in the body. Obtaining biopsies
from each metastasis would be impossible to achieve and scanning the bone marrow for
disseminated tumor cells has proven a difficult and invasive procedure. This is why we need
to turn to less invasive methods of tumor cell collection. Recently there have been several
developments in this field that may prove capable of collecting enough circulating tumor
cells (CTC) to allow biomarker research by methods we discussed before (Gahan, 2010).
Because CTC levels are low, detection requires enrichment and density-gradient
centrifugation followed by separation. The two main approaches for CTC detection are
immunological assays using monoclonal antibodies or PCR-based assays both exploiting
tissue- and/or tumour-specific antigens. (Gerges et al., 2010 ; Müller et al., 2010) Aside from
the possibility to drastically simplify tumor cell collection, this technique holds another
promising prospective. Early detection of CTCs might help identify patients in need of
adjuvant therapies after successful surgical resection of the primary tumor. Both applications
make it a very promising technique that should certainly be developed in the future. Another
option could be to analyze circulating cell-free DNA (cfDNA) shed by tumor lysis. Combined
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with massive parallel tumor sequencing efforts that are currently underway this method
could prove able of revealing many oncogenic driver mutations on a high-throughput level
(Bernards, 2010). Similar to the analysis of CTCs, this technique can also be used for
diagnostic purposes in the clinic, as has already been show in colon cancer (Mead et al.,
2011).
C. Sufficient patient samples. The last tissue related difficulty with biomarker discovery is
finding enough patient samples. To be able to significantly link gene-expression to disease
outcome, trials need at least 40 patients who respond positively to treatment and an equal
amount of non-responders. Currently most phase II clinical trials do not hold such numbers
and increasing the number of patients would be very expensive and slow down development
time. It makes more sense to pre-screen for candidate genes that should be tested in later
phase II clinical trials. Recent developments have enabled us to screen for specific cellular
processes in mammalian cells using functional genetic screens. This allows us to screen for
cancer-drug responses in the most unbiased way using “gain-of-function” and “loss-offunction” configurations to identify promising target genes (Bernards, 2010). This approach
will narrow down the amount of genes that are to be tested in phase II clinical trials as
functional genetic screens can only identify causal relationships between genes and drug
response. By pre-screening for cancer-related genes we can focus our resources on the most
promising entities, thereby reducing the amount of phase II clinical trials and patients
enrolling in them.
The availability of high quality tumor tissue will pose a significant obstacle to biomarker
development, but developments in this sector follow one another in a rapid pace, showing promising
possibilities. As the sector matures, innovative clinical trials will also take some pressure of the ‘tissue
issue’ by reducing the number of trials and patients involved in development of biomarkers and their
subsequent therapies. Such adaptive clinical trials will allow scientists to test for multiple compounds
and biomarkers in a single trial and focus towards entities that show the most promising results as
the trial progresses. Innovative trial designs will pose a steep learning curve for both pharmaceutical
companies and regulatory bodies like the FDA. Adaptive trials will be discussed later in this article as
we continue here by illustrating the influence of regulatory bodies on personalized medicine.
CASE: Agendia, market entry of a genetic test
Agendia is a Dutch molecular diagnostics company, founded in 2003 as a spin-off of the
Netherland’s Cancer Institute (NKI). They market the Symphony™ suite, consisting of
MammaPrint®, a breast cancer recurrence assay, BluePrint®, a molecular subtyping assay,
TargetPrint®, an ER/PR/HER2 expression assay, and TheraPrint®, a therapy selection
assay. The symphony™ suite helps physicians with various cancer related treatment
decisions.
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The MammaPrint® test is a prognostic biomarker that helps predicting the risk of breast
cancer recurrence in the first five years after diagnosis, which is the period in which
chemotherapy produces most of its benefits to a patient. MammaPrint® was developed using
high-throughput microarray analysis of tumor tissue and determines expression profiles of 70
genes to stratify patients at “high risk” or “low risk” of breast cancer recurrence. This gives
physicians a more accurate tool to determine if adjuvant chemotherapy would be advisable.
Because MammaPrint® is superior to conventional tool for selection of these “high risk”
patients the test is able to reduce chemotherapy overtreatment by 27% (Van ’t Veer et al.,
2002; Mook et al., 2007). Moreover MammaPrint® has been demonstrated to be a costeffective strategy to guide adjuvant chemotherapy treatment (Chen et al., 2010). Hereby
MammaPrint® saves patients the detrimental effects of systemic cytotoxic treatment and
relieves some pressure of the great financial burden cancer puts on our society.
MammaPrint® was the first IVDMIA to obtain 510(k) clearance from the FDA and was made
commercially available in 2008 in the US. Agendia has further established excellent coverage
for MammaPrint through Medicare (CMS) and private US health insurance companies.
Surprisingly Agendia is experiencing much greater difficulties in other countries such as the
Netherlands, where the company originated. MammaPrint® is CE-certified and already used
by multiple Dutch hospitals following its inclusion in the updated 2008 guidelines of the Dutch
institute for healthcare improvement CBO. Several leading private insurance companies have
adopted a positive reimbursement policy towards MammaPrint® and the NKI-AVL, a
worldwide acknowledged center of excellence and leading institution in advancing cancer
treatment and care, has declared MammaPrint® as their standard of care for breast cancer
treatment. Nonetheless the Dutch Insurance Governing Body (CVZ) has rejected
MammaPrint®’s inclusion in the basic health insurance package. This decision has great
implications because if there is uncertainty about the ability to recoup the costs of an assay,
labs will not offer it. And if physicians must provide elaborate justification of medical
necessity, the tests will not be ordered. Knowing that coverage is a necessary condition to
commercial success, potential investors will hesitate to finance further developments, making
market entry very challenging, particularly to diagnostics companies like Agendia, that lack
the financial means of big pharmaceutical companies.
CVZ’s rejection decision has been a major setback for Agendia, which is now focusing its
marketing efforts on the US, a clear illustration that more coherent worldwide regulation is
needed to assure that all patients worldwide receive state of the art cancer treatment. The
Dutch CVZ has declared they cannot grant full coverage to a product that is backed only by
retrospective validation and feasibility studies. Rather they await prospective validation in the
large MINDACT phase III trial (Microarray In Node-negative Disease may Avoid
ChemoTherapy), which will take till 2014 before generating its first results. Although the
validity and cost effectiveness of the MammaPrint® have been debated, the test has been
developed and extensively validated using a retrospective approach. Also current clinical
results are very satisfactory, making it clear that more patients could benefit from the test.
Unfortunately, unlike the FDA and CMS, the Dutch CVZ is unable to cope with this innovative
approach to cancer treatment and is reluctant to publicly stimulate its use. Agendia, which
has received multiple healthcare innovation awards, is challenged to great lengths in terms of
financing their attempt to further develop and market their product portfolio.
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A recent attempt for an Initial Public Offering (IPO) was pulled by Agendia due to "an
extraordinarily volatile period in global capital markets resulting in high levels of uncertainty
and volatility", showing that the financing environment for biotech firms is very challenging.
Agendia claims it has collaborated with Roche, Novartis, Sanofi and Pfizer in clinical trials
and is looking to add colon and lung cancer tests to its product portfolio. But to continue
research Agendia will have to find ways to bridge their upcoming financing deficit. Even with
two of their products already generating revenue, they booked a 16.1M euro loss in 2010,
and after a first-quarter loss of 5.26 million euro in 2011, they had about 11M euro in cash on
March 31. At the current burn rate Agendia would need new funding by the end of the year or
resort to other measures, like reducing staff and R&D activities. Further, Agendia could retry
the IPO, which was estimated to generate 75M euro in cash and increase their lobby with the
Dutch government for additional grants and tax incentives.
In a recent elevator pitch Agendia points government officials to the fact that reducing
chemotherapy over-treatment will generate huge revenues because women return to work
much sooner instead of rehabilitating in the hospital or at home. In the Netherlands 13000
women develop breast cancer each year, of which 25% will metastasize at a later stage.
However as much as 75% of women receive adjuvant chemotherapy, meaning that 50% of
all breast cancer patients receive unnecessary chemotherapy. According to Chief Scientific
Officer, Prof. Dr. R. Bernards, PhD, MammaPrint® could reduce chemotherapy by 27%,
calculating to 45M euro in labour productivity per year (Table 1).
Table 1 Net savings to Dutch society after introduction of MammaPrint®. The costs of MammaPrint® testing approximately
even out with the money saved by a 27% reduction in adjuvant chemotherapy. However MammaPrint® will reduce the
amount of lost labour years with 650 years annually, calculating to 45M in savings to the Dutch society per year.
Upon close examination we estimate the net savings to be closer to 65M euro annually based on
recent research on the effects of chemotherapy of women’s working life. We estimate that
because of chemotherapy women have 18 additional weeks of absence from work (16-19 weeks
according to Drolet et al., 2005 and Lauzier et al., 2008) instead of 13 weeks according to
Agendia. The actual total savings could be even higher as elder women (age > 51) are 1,9 times
more likely to go on long-term disability, stop working, or retire because of the effects of
chemotherapy (Hassett et al., 2009).
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Also, one year after diagnosis all breast cancer patients have lost 27% of their projected
annual wages (Lauzier et al., 2008), a number which could be reduced upon full introduction
of MammaPrint® in the Dutch healthcare system. Given the growing role of women on the
labour market and the aging of our workforce it seems logical for the Dutch government to
invest in healthcare products that can take some pressure of the social and economic burden
that cancer puts on our society
Learning points:

The current regulatory framework relies on timelines and methods that are
mismatched to recent innovations in the diagnostics field, evidence requirements
are not in line with modern methods of biomarker development.

Diagnostics companies need more government support to bridge their market
entry phase, buying time to earn back their development costs and reassure their
investors. Personalized medicine can introduce cost-effective changes to our
healthcare system, giving governments a good return on their investments while
pushing cancer treatment to the next level.
2.3 Regulatory clarity
Biomarkers or in vitro diagnostics (IVDs) often come as complex multi-gene genomic tests that use
algorithms to calculate probable treatment outcome for patients. Such tests have been categorized
by the FDA as ‘in vitro diagnostic multivariate index assays’ (IVDMIA) in the 2007 guidelines for
IVDMIAs. However, the guidelines are still not codified by law and leave IVDs heavily debated. The
algorithms, doing the math for physicians, form a black box that cannot be independently validated.
This has raised voices for stronger regulation of IVDs; while others fear regulation would stifle
innovation. It may seem over-done to regulate the blueprints of genomic tests, as long as they can
significantly improve treatment (Majewski et al., 2011). Ideally the regulatory trajectory for
biomarkers should focus on three aspects of the test, namely analytical validity, clinical validity and
clinical utility but also keep in mind the associated ethical, legal and social implications. The US
Center for Disease Control and prevention (CDC) has developed the ACCE model (Figure 4) that is
composed of a standard set of 44 targeted questions taking all these factors into account (Haddow et
al., 2003). When IVDs prove viable in these areas they should be granted market access, regardless of
their algorithms or other forms of internal clockworks, protecting companies’ intellectual properties
whilst safeguarding patients’ health and improving treatment outcome.
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Figure 4 ACCE Model Process for Evaluating Genetic Tests (as published by CDC)

Analytic validity: A test’s ability to accurately and reliably measure the genotype of interest. Analytic validity
focuses on the laboratory components of testing, including analytic sensitivity, analytic specificity, laboratory
quality control and assay robustness.

Clinical validity: A test’s ability to detect or predict the associated disorder (phenotype), including clinical
sensitivity (or the clinical detection rate), clinical specificity and positive and negative predictive values. Clinical
validity is affected by the prevalence of the disorder, penetrance, analytic sensitivity and genetic and
environmental modifiers.

Clinical utility: A test’s ability to affect clinical decisions and patient outcomes in practice. Other elements or
contextual factors to be considered include the natural history of the disorder, availability and effectiveness of
interventions, quality assurance, health risks of testing or resulting interventions, financial impacts of testing,
adequacy of facilities to provide services, availability of patient and provider education and monitoring and
evaluation of test performance in practice.
Nonetheless the lack of coherent regulation should be resolved quickly, as it creates a dangerous
situation with companies using loopholes to market their IVDs as so called ‘laboratory developed
tests’ (LDTs). LDTs are overseen by the 1988 Clinical Laboratory Improvements Amendments for use
in a single laboratory, not as tightly regulated as regular medical tools. Further, the effects of
variation in laboratory practices can greatly influence the reliability of these tests (Majewski et al.,
2011). Physicians basing vital treatment decisions on complex genetic tools that are so poorly
regulated is a recipe for disaster, urgently requiring more stringent regulation policies.
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Another example is the lack of coherent reimbursement regulations. The current procedures to
obtain reimbursement status for an IVD product tend to be complex and vary greatly from one
country to another. In the US, the Center for Medicare and Medicaid Services (CMS) plays a lead role
in setting reimbursement for IVDs. CMS develops coverage policy for over 46 million Medicare
beneficiaries, but all private payers also benchmark CMS coverage decisions in establishing their own
policies. However, the CMS fails to establish clear evidence criteria for coverage, basing their decision
on the fact if an IVD is “reasonable and necessary for the diagnosis or treatment” (CMS, 2006). CMS
provides little specific guidance about the type and strength of evidence that would suffice, and
therefore the means by which CMS and its regional contractors make coverage determinations lacks
evidence standards that can be clearly understood by IVD innovators. Thus, to be reasonably certain
of success an IVD innovator must anticipate meeting a very high standard, which likely means
multiple prospective trials, each requiring numerous sites and a large number of subjects which is
particularly difficult when aiming for a smaller patient subgroup. Investors require a clear view on
their future return on investment and the risks that are involved. Because current reimbursement
regulations obscure this view, R&D investments are now mainly driven by drug manufactures codeveloping IVDs for their targeted therapeutics. The reimbursement framework relies on timelines
that progress far slower than the pace of IVD development and evidence requirements that are
mismatched with the clinical and economic realities of IVD development (Parker, 2010). Standards of
evidence must be made clear to companies in order to understand and better predict coverage
decisions. This will make investments in IVDs more attractive, thus stimulating the path towards
personalized medicine.
Despite the remarks I made on current US policies, it has to be noted that the FDA has taken a
leading role amongst government organizations worldwide in stimulating developments in the PM
sector. The FDA’s Critical path initiative in 2004 was the first major step to make product
development more predictable and less costly (FDA, 2006). Other governmental bodies such as the
EMEA were quick to adopt similar policies. However it took till 2011 for the FDA to spend $25 million
USD to “identify improved pathways to product development and approval for new technologies that
offer promising new opportunities to diagnose, treat, cure and prevent disease” (FDA, 2011). This
illustrates the FDA’s own realization that companies are still having great difficulties finding their way
through the maze of regulatory obligations before getting their products to the market. Let alone the
regulatory differences between individual continents or countries. So although governmental
incentives to ease market access for targeted therapeutics and their companion diagnostics are
improving, there is still a long way to go. Governmental bodies and pharmaceutical companies
should work together to clarify and take away some of the risks associated with the large, upfront
investments in targeted therapeutics. Only then will personalized medicine be strategically favorable
and financially viable.
2.4 Getting the right mindset
The final obstacle is of another kind, namely that some pharmaceutical companies seem reluctant to
focus on targeted therapies, clinging on to their ‘Blockbuster’ business model in fear of losing profits
to reductions in their eligible patient subgroups. For the past 40 years they have been relying on a
few blockbuster drugs for the bulk of their revenues, dictating their entire strategic direction.
Historically this has allowed the industry to enjoy consecutive years of double-digit growth, but
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following recent developments in pharmacogenomics the blockbuster business model has come
under heavy pressure. The model aims for the entire population, thus relying on very large clinical
trials to demonstrate significant improvement among many non-responders and adverse events.
With capitalized costs of development currently estimated between $161 million USD and $1,8
billion USD (Morgan et al., 2011) this model is just no longer sustainable. Even though the promises
of personalized medicine seem clear and the industry is widely advocating their developments in this
field, they are still reluctant to make a dedicated switch to targeted therapeutics. A good example of
this paradoxical behavior is the case of the Sanofi-Aventis Poly (Adenosine diphosphate–Ribose)
Polymerase 1 (PARP1), inhibitor Iniparib (O’Shaughnessy et al., 2011). Sanofi-Aventis recently
announced that a Phase III trial evaluating Iniparib in patients with metastatic triple-negative breast
cancer (mTNBC) failed to meet its primary endpoint. The 519-patient study failed to show significant
benefits on overall survival and progression-free survival from adding Iniparib to standard
chemotherapy comprising gemcitabine and carboplatin. These negative results came as bad news as
Iniparib had been projected to reach peak annual sales in excess of $1 billion USD. A closer look at
the failing Phase III study however gives us an insight in Sanofi-Aventis reluctance to dedicate the
drug solely to the appropriate patient subgroup. Accounting for 15 to 20% of all cases of breast
cancer, triple-negative breast cancer (TNBC) shares clinical and pathological features with hereditary
BRCA1-related breast cancers, but requires a biomarker for stratification. Sanofi- Aventis failed to
determine a biomarker for Iniparib, which was not necessary to get through Phase II clinical trials,
probably because of the small patient cohorts. But because they were unable to accurately select
patients that would benefit from their drug in Phase III, this strategy did prove a mistake.
AstraZeneca with their Olaparib, did go through the process of determining a biomarker (Lau et al.,
2009) and has since had more success in BRCA1/BRCA2 positive breast cancer. It should be noted
that recent data indicate that Iniparib is not a good PARP inhibitor at all; raising questions whether
this drug would even be successful in BRCA mutant breast tumors. This example still illustrates the
point that some pharmaceutical companies are reluctant to effectively narrow down their patient
population and target their drugs only to the patients that would benefit.
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3. Viability of personalized medicine
There is an ongoing debate on the financial viability of personalized medicine. Given the obstacles
that we have already described it is clear that there still is a long way to go. So before committing
ourselves to this new way of treatment it is wise to go over the financial viability of personalized
medicine in order not to waste our recourses. There are a number of changes to the conventional
way of drug development and its revenue models, which I think can turn out very favorable for all
parties involved. First, I will focus on changes in the development of drugs and later we will see how
personalized medicine can generate a good return on investment.
3.1 Adaptive clinical trials
Most clinical trials with cancer drugs follow the same development path. First their safety and
pharmacodynamic properties are evaluated in phase I clinical trials with late-stage cancer patients.
Then follow single-arm or sometimes randomized phase II trials that focus on specific tumor types to
test the workings of the compound. When all is well, companies will seek regulatory approval
through phase III trials and bring the drug to the market. This is a long and painstaking process that
costs pharmaceutical companies many resources, especially in late-stage clinical trials. Following the
‘blockbuster business model’, investments in failed compounds are to be earned back with
blockbuster drugs that do succeed, indirectly raising the costs of clinical trials tenfold. Surprisingly
the relative amount of compounds that make it to the market is still as low as it was 25 years ago. In
fact there is a 90% failure rate in reaching phase III from phase I (Blair, 2010).
In an attempt to stimulate more innovative
clinical trial designs the Food and Drug
Administration (FDA) has released the critical
path initiative in 2004 and critical path
opportunity list in 2006. This was done in
response to stagnation in the development
of New Molecular Entities (NMEs) and the
ever growing amount of compounds that
enter trial but never make it to the market
(Woodcock, 2005). The FDA makes strong
recommendations
promoting
adaptive
clinical trials and the potential use of Figure 5 Validity and integrity of adaptive clinical trials.
Bayesian statistical methods for the development of new compounds and their accompanying
biomarkers (FDA, 2006). This issue is further stressed by the European Medicines Agency (EMEA),
who issued a similar paper in 2006 concerning confirmatory clinical trials with flexible design and
analysis plan (EMEA, 2006). The FDA defines adaptive trials as “a study that includes a prospectively
planned opportunity for modification of one or more specified aspects of the study design and
hypotheses based on analysis of data (usually interim data) from subjects in the study.” This means
that the accumulating data is analyzed at pre-defined timepoints, following evaluation of the
hypothesis and possible adaptations to optimize the future course of the trial. This ought to be done
under strict statistical testing to ensure proper conduct (FDA, 2010). Here I see regulatory concerns,
fearing for the integrity and validity of clinical trials when allowing high levels of flexibility. This in
16 | P a g e
turn will pose a threat to pharmaceutical companies who will be hesitant to go through the effort of
designing expensive adaptive trials with the risk of being rejected due to regulatory ambiguity. To
overcome this pitfall the FDA encourages pharmaceutical companies to seek earlier and more
comprehensive interaction with regulatory bodies like themselves.
The following types of adaptive trials can be distinguished, based on the parameters that can be
adjusted during the trial; the adaptive randomization design, the group sequential design, the sample
size re-estimation design, the drop-the-loser design, the adaptive dose finding (e.g., dose elevation)
design, the biomarker-adaptive design, the adaptive treatment-switching design, the hypothesisadaptive design, the adaptive seamless phase II/III trial design, and the multiple adaptive design.
More details on these trial designs have been described by Chow et. al, 2008, making it clear that
adaptive trials can play a vital role in clinical research and development. The biomarker-adaptive
design is obviously of specific interest to the progress of personalized medicine given that biomarkers
play such an important role for targeted therapeutics. But I expect that other specific traits of
adaptive clinical trial design are vital tools in keeping personalized financially viable.
Advantages of adaptive designs. The most clear advantage of adaptive trials is the fact that they
allow for ‘on the go’ modifications such as changing endpoints or dose adjustments in response to
results that could not be foreseen at the start of the trial, as long as they are within the
predetermined boundaries of variability. Moreover the adaptations will be pre-approved by
regulatory bodies and ethics committees so there is no need to file protocol amendments when
changes are executed. Logistics for changing treatments or doses can also be planned upfront. And
finally there is a broad regulatory acceptance, particularly in case of exploratory adaptive design
clinical trials for prove of concept studies (Mahajan et al., 2010). Adaptive trials would allow testing
of more doses in the same Phase I and II trials, helping physicians to better understand the effect of
the novel compound and the clinically relevant doses. This allows pharmaceutical companies to set
up more effective Phase III trials and reduce compound attrition at this stage or maybe worse, at
later stages of the development process. Thus, trial subjects are used more efficiently and fewer
subjects are given ineffective compounds, ineffective doses or doses that are unnecessarily high. So
pharmaceutical companies waste less on unsuccessful compounds and can quickly re-assign
resources to alternative drugs within their pipeline (Mahajan et al., 2010). Unlike today, this would
save patients from exposure to harmful or ineffective doses and experimental treatments, thus
presenting a strong advantage to patients as well. The overall effect of adaptive trial designs will be
that drug development becomes less time consuming, cheaper and more favorable to all parties
involved.
Disadvantages and risks associated with adaptive designs. Given their complexity, the
implementation of adaptive trials can pose a risk for pharmaceutical companies new to this
approach. The highest risk would be making mistakes due to inexperience or failing the trial when
not being properly aligned with governmental agencies. Further, the use of Bayesian statistical
analysis is compulsory after adaptations rather than free choice and Bayesian statistical methods are
still considered non-standard. Another risk is that ‘on the go’ changes based on unblinded data may
jeopardize the credibility of the study. Even EMEA’s paper has highlighted the risk of damaging the
integrity of a trial due to frequent interim analyses. There is also the risk of adapting trials too early,
thereby jeopardizing the overall study findings. Above all, overall regulatory acceptance and
experience is still far from sight (Mahajan et al., 2010).
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Before getting started, the EMEA underlines two important issues to minimize the risks associated
with ‘on the go’ adaptations to clinical trials. First of all there should be a strong need to reevaluate
the study objectives. Secondly the number of interim analyses should be well founded by forehand.
Researches should find a balance between the need to assess the accumulating data at interim
points, while maintaining the integrity of the trial. “Routinely breaking the blind should be avoided,
particularly when it can be foreseen that insufficient information will be available for stopping the
study because of proven efficacy or futility or meaningful safety concerns of the experimental
treatment” (EMEA, 2006).
Recently, the Biomarkers Consortium launched a pioneering multi-agent adaptive clinical trial to
treat breast cancer, intended to give several investigational drugs to treat breast cancer together at
the same time, under a project named “Investigation of Serial Studies to Predict Your Therapeutic
Response with Imaging And Molecular Analysis (I-SPY 2 TRIAL).” The Biomarkers Consortium is a
unique public–private partnership led by the Foundation for the National Institutes of Health (NIH).
In this trial, adaptive design will enable researchers to use early data from one set of patients to
make decisions about which treatments might be more useful for patients later in the trial and
eliminate ineffective treatments more quickly (The Biomarker Consortium, 2010; Jones, 2010).
Further the trial has been designed to match patients to drugs that will likely respond, based on the
molecular characteristics of their tumors, found earlier in the study. This revolutionary approach is
supposed to greatly reduce the number of patients necessary to significantly demonstrate the clinical
end-points of the study. This principle will be elaborated on in the Vemurafenib case, which applies a
similar approach.
So as this type of clinical trials is in its infancy, caution is still advised. Companies should not turn to
adaptive designs as a solution for poor planning in an attempt to save their trials. Also the potential
for improved efficiency comes at a price; compared with more traditional trial designs, adaptive
approaches require more work and additional effort during planning, implementation, execution,
and reporting (Quinlana, 2010). Although, presently, there might be some problems in the execution
of adaptive designs, with the release of the draft guidance for industry on adaptive design clinical
trials, more and more companies are bound to use adaptive designed clinical trials, thus making the
drug development process shorter and cheaper.
Figure 6 Comparison between conventional trial and adaptive design trial. Adaptive clinical trials differ from conventional
trials in a number of ways, such as their flexibility, sizes and clinical end points. (Modified from Mahajan et al., 2010)
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3.2 Drug development costs
The pharmaceutical industry is under great pressure, demonstrated by more or less flat share prices
for the past 7 years and disappointing innovative behavior. Between 1990 and 2005, 920 cancer
compounds underwent clinical trials, yet only 32 were approved (Reichert et al., 2008), without a
dramatic increase in R&D productivity, today's pharmaceutical industry cannot achieve sufficient
innovation to replace the loss of revenues due to upcoming patent expirations. Key patent
expirations between 2010 and 2014 have already been estimated to put more than $209 billion USD
in annual drug sales at risk, resulting in $113 billion USD of sales being lost to generic substitution
(EvaluatePharma, 2009). Among the challenges faced by pharmaceutical companies, the most
important aspect will be to raise R&D productivity and efficiency. In order to evaluate the changes
personalized medicine can have on this process we need to have a model that is able to generate a
well founded calculation on the precise costs of drug development and that is able to cope with the
different variables that PM will change. For example the Boston Consulting group estimates that
before genomics technology, developing a new drug has cost companies on average $880 million, 15
years from start to finish, with about 75% attributed to failures along the way. By applying genomics
technology, companies could on average realize savings of nearly $300 million USD and two years per
drug, largely as a result of efficiency gains. Representing a 35% cost and 15% time saving (BCG, 2001).
Strong debate and variation surrounds all calculations on the actual costs of drug development.
Estimates of the costs to bring a drug to market vary between USD$92 million cash ($161 million USD
capitalized) to $883.6 million USD cash ($1.8 billion USD capitalized) (Morgan et al., 2011) (Figure 7).
Figure 7 Estimates of the components of drug development costs from 5 leading studies. Estimates vary between $92
million USD cash ($161 million USD capitalized) to $883.6 million USD cash ($1.8 billion USD capitalized). A more complete
overview of recent studies can be found in appendix 2. (Modified from Morgan et al., 2011).
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CASE: Vemurafenib, small Phase III clinical trial design
Metastatic melanoma has a poor prognosis, with the median survival for patients with stage
IV melanoma ranging from 8 to 18 months after diagnosis, depending on the substage.
Vemurafenib (PLX4032) is a potent inhibitor of mutated BRAF. It has marked antitumor
effects against melanoma cell lines with the BRAF V600E mutation but not against cells with
wild-type BRAF. In a recently published study with only 675 patients that compared
Dacarzine with Vermurafenib it demonstrated median progression-free survival of 5.3 months
in the Vemurafenib group and 1.6 months in the dacarbazine group. At 6 months, overall
survival was 84% (95% CI, 78 to 89) in the vemurafenib group and 64% (95% CI, 56 to 73) in
the dacarbazine group. Further, the difference in confirmed response rates between the two
study groups (48% vs. 5%) was highly significant. (Chapman et al., 2011)
Significantly demonstrating a strong survival difference with only 675 patients in a Phase III
clinical trial is unseen in the industry. Normal Phase III clinical trials range between 10005000 patients, but with the power of patient stratification this number can be greatly reduced.
Smaller Phase III clinical trials mean a strong reduction in development costs. But maybe
even more important is that fact that companies can charge premium prices for drugs with
such strong difference in survival and high response rates.
3.3 Economic modeling of R&D productivity
Even studies that estimate a more modest total capitalized cost of development like Adams and
Brantner, who calculate an average of $1074.3 million USD for the period of 1989 and 2002, found
that some New Molecular Entities (NMEs) take well over $2.5 billion USD to develop (Adams and
Brantner, 2006). For the following calculations I stick to the model proposed by Paul et al, which
differentiates itself mainly on high attrition rates and high capitalized development costs of $1799.6
million USD to bring a drug to market. This number is much higher than the generally accepted $1
billion USD, but it has to be noted that $1 billion USD is the result of studies on the period between
1990 and 2000 and that development costs tend almost to double every 10 years for the past 50
years (Appendix 1, Morgan, 2011). This model has been constructed using recently published R&D
performance data from a group of 13 large pharmaceutical companies, provided by the
Pharmaceutical Benchmarking Forum (Paul et al., 2010). Further this model has been designed to
calculate the effects of strategic changes on the total capitalized costs of drug developments and will
conveniently serve my own calculations. Thus I feel the $1.8 billion USD has been well substantiated,
especially as cancer drug development has particularly high attrition rates and relative high
associated costs (Booth et al., 2003). In this model, clinical development (Phases I–III) accounts for
approximately 63% of the costs for each NME launched, and preclinical drug discovery accounts for
32%. The process of discovering and developing an NME on average required approximately 13.5
years (yearly averages ranged from 11.4 to 13.5 across 2000–2007). Notice how the value of money
makes up more than half of the investment in a NME by comparing out of pocket costs to capitalized
20 | P a g e
costs. This effect buffers the relative high costs of late-phase clinical trials compared to early stage
development because this money is invested much earlier in the project and is thus ‘more expensive
money’.
Figure 9 R&D model for the costs of successfully developing and marketing a NME. The model defines the distinct phases
of drug discovery and development from the initial stage of target-to-hit to the final stage, launch. R&D parameters
include: the probability of successful transition from one stage to the next (p(TS)), the phase cost for each project, the cycle
time required to progress through each stage of development and the cost of capital, reflecting the returns required by
shareholders to use their money during the lengthy R&D process, also named the value of money. With these inputs
(darker shaded boxes), the model calculates the number of assets (work in process, WIP) needed in each stage of
development to achieve one NME launch. Based on the assumptions for success rate, cycle time and cost, the model
further calculates the 'out of pocket' cost per phase as well as the total cost to achieve one NME launch per year (US$873
million). Lighter shaded boxes show calculated values based on assumed inputs. Capitalizing the cost, to account for the
cost of capital during this period of over 13 years, yields a 'capitalized' cost of $1,778 million per NME launch. (Modified
from Paul et al., 2010)
R&D productivity can be simply defined as the relationship between the value (medical and
commercial) created by any NME and the investments that are required for its development. With
this definition in mind, Paul et al., have created the 'pharmaceutical value equation', which includes
the key elements that determine both the efficiency and effectiveness of the drug discovery and
development process for any given pipeline). For example, having sufficient pipeline WIP is crucial
given the substantial attrition rates. However, increasing WIP (especially late-phase) alone will
undoubtedly increase C and may also increase CT, which could further reduce P, thus diminishing
productivity.
Figure 8 Pharmaceutical value equation. R&D Productivity (P) can be viewed as a function of the elements comprising the
numerator — the amount of scientific and clinical research being conducted simultaneously, designated here as the work in
process (WIP), the Probability of Technical Success (p(TS)) and the Value (V) — divided by the elements in the denominator,
the Cycle Time (CT) and Cost (C). Thus, if one could increase the p(TS) (that is, reduce attrition) for any given drug candidate,
P would increase accordingly. If one could cut development time (CT) or costs (C), the productivity (P) would increase even
more. (Modified from Paul et al., 2010)
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Figure 10 Parametric sensitivity analysis. Calculates the capitalized cost per launch based on assumptions for the model's
parameters (the probability of technical success (p(TS)), cost and cycle time, all by phase). When baseline values for each of
the parameters are applied, the model calculates a capitalized cost per launch of US$1,778 million. parameters are varied
from 50% lower and 50% higher relative to the baseline value for cost and cycle time and approximately plus or minus 10
percentage points for p(TS). Once cost per launch is calculated for the high and low values of each parameter, the
parameters are ordered from highest to lowest based on the relative magnitude of impact on the overall cost per launch,
and the swings in cost per launch are plotted on the graph. At the top of the graph are the parameters that have the
greatest effect on the cost per launch, with positive effect in blue (for example, reducing cost) and negative effect in red.
Parameters shown lower on the graph have a smaller effect on cost per launch. (Modified from Paul et al., 2010)
With this model we can investigate which parameters have the strongest contribution to the
capitalized cost of one NME of $1.78 billion USD. As Figure 10 demonstrates, attrition rates p(TS) of
late phase clinical trials have the biggest impact on R&D efficiency. In the baseline model, Phase II
p(TS) is 34% (66% of compounds fail in Phase II). If Phase II attrition increases to 75% (a p(TS) of only
25%), then the cost per NME increases to $2.3 billion USD, or an increase of 29%. Conversely, if
Phase II attrition decreases from 66% to 50% (that is, a p(TS) of 50%), then the cost per NME
decreases by 25% to $1.33 billion USD.
Similarly, our baseline value of p(TS) for Phase III molecules is 70%; that is, an attrition rate of 30%. If
Phase III attrition can be reduced to 20% (80% p(TS)), then the cost per NME will be reduced by 12%
to $1.56 billion USD.
Work in progress (WIP). Companies need to have enough WIP or products in their pipeline to secure
their R&D productivity. Given the high attrition rates a company would need 8.6 WIPs to get a single
NME to market. However with conventional strategies an increase in WIP would also result in an
increased CT, diminishing the positive effects on P, especially if development resources become ratelimiting. To overcome this, companies should try to allocate more resources to early stage
development, ideally by redirecting them from entities that are doomed to fail in Phase III (or even
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Phase IV). Adaptive clinical trials designed for targeted therapeutics are ideally suited for this as they
allow testing of more doses in the same Phase I and II trials, allowing companies to prepare for more
efficient Phase III trials and reduce attrition rates at later stages. After the first results surface,
resources can be directed towards the most promising WIPs, while abandoning trial arms that seem
unsuccessful. Given the C and CT of a single Phase III unit of WIP ($150 million USD), almost 10 Phase
I molecules ($15 million USD) can be developed for the same cost, ideally through to proof-ofconcept studies (Paul et al., 2010). Biomarkers and surrogate end points are inextricably linked to this
approach.
Another way to substantially increase early WIP is through outsourcing. Traditionally, large
pharmaceutical companies have managed discovery, development, manufacture and
commercialization of their medicines mainly in-house. Today, virtually all elements of R&D can be
partnered or outsourced to substantially improve R&D productivity by affordably enhancing the
pipeline from early discovery through to launch. This will theoretically allow greater access to
intellectual property, molecules, capabilities, capital, knowledge and of course, talent (PWC, 2008).
Complex ICT infrastructures used in personalized medicine will facilitate this process of outsourcing
and even collaboration.
Value (V). Patients, physicians, payers and healthcare officials apply different criteria to determine
the value of a new drug. Payers will be increasingly interested in clinical trial data that prove the
efficiency, but also cost effectiveness of a certain NME. Patients on the other hand will be primarily
interested to be cured from their disease and retain a high quality of life. This high value creation is
exactly the promise of PM. Targeted therapeutics will prove more effective, sparing patients the ‘trial
and error’ approach of treatment selection, saving vital time and suffering. Clearly PM can increase a
company’s R&D productivity here.
Cycle time (CT). Reducing CT has long been the management goal of any production system.
However in the unpredictable pharmaceutical R&D setting, proven concepts cannot be broadly
adapted. Again we make claim for the use of adaptive and seamless Phase II and III study designs that
can prove extremely useful in reducing clinical CT, generally by reducing non-value-added wait times
between phases of development (Paul et al., 2010). Further, 71% of oncology drug approvals were
given a priority review rating by the FDA, in contrast to 40% for other new drugs. Also sponsors of
oncology drugs were much more often able to take advantage of at least one of the FDA’s programs
to speed development (subpart E, accelerated approval, fast track) (DiMasi et al., 2007). Finally there
is the critical path initiative and following measure by the FDA aimed at facilitating market entry for
innovative compounds. Especially in early development, time saving will have great impact on the
capitalized costs and overall time-saving will leave a longer period to get a return on investments
(ROI) before patents run out.
Costs (C) Unit cost reductions, like CT reductions, can be leveraged to improve productivity but
without the implementation of PM it will be difficult to greatly reduce C of R&D activities. However
as the cases of Vemurafenib and the I-SPY 2 TRIAL clearly demonstrate, targeted therapeutics does
yield the possibility to greatly reduce development costs in Phase III clinical trials. By selecting the
right patient subgroups for a drug in clinical trials, based on their genetic makeup, compounds will be
sure to show great response rates and improved survival differences. Following this principle, clinical
trials require far less patients to enroll to demonstrate significant results, even if the difference in
23 | P a g e
survival is initially not so strong. Vemurafenib was able to demonstrate significant results using only
675 patients, instead of 1000-5000 commonly used in Phase III trials. Another way of cost reduction
is by finding alternative financing methods (Douglas, 2008), like attracting additional cash flows. As
we demonstrated in the Agendia/MammaPrint case, PM will relieve some of the ever growing
pressure that cancer puts on our society. It would be advisable for governments to install financial
incentives for the development of personalized medicine, thus reducing the costs of R&D
development with sponsorships.
Probability of technical success (p(TS)). By now it will be crystal clear that reducing the attrition rate
of drug candidates in clinical development represents the greatest opportunity for pharmaceutical
R&D, and arguably for sustaining the viability of the entire industry. As the sensitivity analyses in
Figure 10 shows, reducing Phase II and III attrition are the strongest levers to reduce the costs per
NME (Paul et al., 2010). Non-technical attrition as a result of strategic or commercial decisions is not
relevant in this case. Instead, lack of efficacy and low margins of safety are the major causes of Phase
II and III technical attrition (Kola & Landis, 2004). There are two solutions to this problem. First is
better target selection as demonstrated by the I-SPY 2 TRIAL. When our understanding of genetic
onset of disease increases we will be able to much better predict which segments of patient
populations will respond to certain targeted therapeutics and aide us in the decision to commit
substantial time and resources to a drug. The second solution to attrition is in the routine pursuit of
early proof of concept studies, especially in Phase I, for which biomarkers and surrogate endpoints
can often be employed. These measures will be necessary to make early, but well informed 'go/nogo' decisions.
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3.4 Calculating the potential cost reductions of personalized medicine
The economic R&D model proposed by Paul et al. allows us to make accurate calculations on the
effects that PM can have on the capitalized development costs for targeted therapeutics. To do this
we will make a number of assumptions and adapt the chances to the baseline model Figure 10.
Cost reducing:
 p(TS) Phase II to 50%
 p(TS) Phase III to 80%
 CT Phase II-III reduced by one year
 p(TS) Submission to launch to 100%
 C Phase III reduced to 75M
 CT Submission to launch reduced by 0.5 year
$1135M
$500M
$200M
$100M
$125M
$175M
$35M
Cost increasing:
 p(TS) Phase I to 50%
 C Phase I increased to 20M
$400M
$350M
$50M
Total potential capitalized savings:
Baseline capitalized cost per NME:
New capitalized costs per NME:
$735 million USD
$1799.6 million USD
$1065 million USD
Figure 11 Estimating new cost per NME for personalized medicine. When all the potential benefits of personalized
medicine are achieved the model suggests that the development costs of a targeted therapeutic can be reduced by ~40%
to $1065 million USD. Some costs of development decrease, with a total of $1135 million USD, while others increase with a
total of $400 million USD.
The modeling reveals that the development costs of a targeted therapeutic can be reduced by ~40%
from $1799 million USD to $1065 million USD if all the potential benefits for PM development can be
achieved. Such large reductions in development costs built a strong case for pharmaceutical
companies to explore these promising possibilities and start adopting measures to keep their
businesses sustainable in the future.
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CASE: Iressa, Tarceva , Herceptin and the value of a biomarker.
To get a good impression of the effects of a biomarker on the development, market approval
and sales of a drug we compare the stories of Iressa, Tarceva and Herceptin. Iressa and
Tarceva are both EGFR Tyrosine Kinase Inhibitors (TKIs), indicated for treatment of NonSmall Cell Lung Cancer (NSCLC) and Herceptin is a monoclonal antibody directed against
Human Epidermal growth factor Receptor 2-positive (HER2+) breast cancer cells.

Iressa (Gefitinib) is an AstraZeneca TKI that received FDA´s accelerated market
approval in 2003 but failed to demonstrate significant survival benefits after which the
FDA decided to change its indication. Ever since, AstraZeneca has been struggling to
gain market approval by searching for a biomarker that could successfully stratify
patients that could benefit from the drug. Recently AstraZeneca has been able to
demonstrate EGFR to be a good biomarker and gained market approval for Iressa in the
EU and USA.

Tarceva (Erlotinib hydrochloride) is marketed in the US by Astellas Pharma´s OSI
Pharmaceuticals and Genentech, a wholly owned subsidiary group of Roche, who does
the marketing elsewhere. Although Tarceva is clinically comparable to Iressa (CVZ,
2010) it did demonstrate a significant survival benefit and was granted market approval
by the FDA in 2004. Even without an appropriate biomarker Tarceva has been the only
TKI on the market for the following 5 years, further illustrating pharmaceutical companies
struggling with biomarkers for their drugs.

Herceptin (Trastuzumab) is marketed by Roche and was FDA approved in 1998 as one
of the first drugs to leverage the power of genetics. Its success is largely attributable to
its accompanying biomarker as it is only prescribed to patients whose genetic tests
reveal an over-expression of the HER2 protein, an indication aggressive cancer that is
responsive to treatment by the drug. Herceptin can be considered an ultimate success
story of targeted therapy that has generated huge revenues for its developer and is very
successful in treat its indicated patients.
Lung cancer. According to the World Health Organization, there are more than 1.6 million
cases worldwide of lung and bronchial cancer each year, causing approximately 1.4 million
deaths annually. According to the National Cancer Institute, lung cancer is the leading cause
of cancer-related death worldwide. Approximately 75 to 80% of all cases of lung cancer are
non–small-cell lung cancer (NSCLC). Advanced-stage NSCLC is currently considered an
incurable disease for which standard chemotherapy provides marginal improvement in
overall survival at the expense of substantial morbidity and mortality (Cataldo et al.,2011;
Hansen et al., 2002).
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The recently developed TKIs, Iressa and Tarceva, are targeted therapies that are indicated
for treatment of NSCLC in patients with activating EGFR mutations. EGFR is a receptor
protein that extends across the cell membrane. EGF binds the extracellular part of EGFR
leading to activation, which triggers a complex signaling cascade that leads to accelerated
cell growth, division and metastasis. It is estimated that as many as one in ten (10%) lung
cancer patients in the Western population and one in three (30%) Asian patients with lung
cancer have NSCLC with EGFR activating mutations (Rosel et al., 2009; Mitsudomi et al.,
2006) making them suitable patients for Iressa and Tarceva.
Iressa. Iressa, the first EGFR TKI for NSCLC was granted accelerated approval by the US
FDA in May 2003 for use only after two rounds of standard treatment with platinum-based
drugs and docetaxel (Cohen et al., 2004), based on promising results of phase I/II studies
(Fukuoka et al., 2003; Kris et al., 2003). However, in the post-marketing Phase III study (Trial
709 or ISEL) Iressa failed to produce a survival benefit among NSCLC patients compared to
placebo (Thatcher et al., 2005). Retrospective tumor analyses by the Massachusetts General
Hospital and the Dana-Farber Cancer Institute showed that eight of nine patients who had
responded to Iressa had EGFR mutations. No mutations were detected among the seven
patients who did not respond to the drug. Using cell culture experiments they hypothesized
that EGFR might have been a distinctive biomarker amongst these patients. “Iressa was a
targeted therapy before the target was really known,” remarked Matthew Meyerson, M.D.,
Ph.D., a member of the Dana Farber team. So, in 2005 the FDA issued a new label for Iressa
limiting its use to “patients with cancer who in the opinion of their treating physician are
currently benefiting (AstraZeneca estimated 15000 patients) or have previously benefited
from Iressa treatment.” On December 17, 2004, AstraZeneca announced the disappointing
ISEL results and subsequent label change in a press release and a “Dear Doctor” letter
(Appendix 2). Further, AstraZeneca withdrew its European Marketing Authorization
Application (MAA) for gefitinib to treat patients with NSCLC from the EMEA. This resulted in
a reduction of 58% in new prescriptions written for Iressa, and 86% of physicians treating
NSCLC modified their treatment practice.
The case of Iressa clearly demonstrates the detrimental effects of marketing a targeted
therapeutic whilst failing to appoint an appropriate biomarker. Sales figures for Iressa
plummeted and have not been recovering so far. In fact in 2009 the FDA has partially
withdrawn Iressa from the market forcing AstraZeneca to focus on the EU market in trying to
revive Iressa, this time with an accompanying biomarker. Iressa sales figures will be
compared to Tarceva later in this case. Aside from clear losses in sales figures,
AstraZenecas company image has been severely damaged by measures such as their press
release, the “Dear Doctor” letter sent to ~141,000 physicians and other healthcare providers,
advertisements in major medical and oncology journals and direct communications to all
known Iressa patients. It is difficult to attach an exact financial figure to the fact that patients,
physicians and other healthcare officials lose confidence in a company and its products. In
2004 the pharmaceutical industry spent 24.4% of its incomes on promotion, versus 13.4% on
R&D, as a percentage of US domestic sales of $235.4 billion USD (Gagnon et al., 2008). The
fact that pharmaceutical companies spend almost twice as much on marketing compared to
R&D does give us good insights on the size of the damage to AstraZenecas branding efforts
because of the Iressa label change.
27 | P a g e
Tarceva. Alongside ISEL ran a Phase III study for Tarceva (BR.21) that compared it with
placebo for patients failing at least one chemotherapy regimen. Tarceva turned out to be
superior to placebo for progression-free survival and objective response rate. Interestingly,
the HRs for Tarceva in the two patient subsets in which Iressa appears to confer benefit,
Asians and never-smokers, were 0.61 and 0.42, respectively (Shepherd et al., 2005). In
contract to ISEL, Tarceva did prolong survival for most of the other patient subsets, although
the sample size is too small in some of the subsets to draw a definitive conclusion. Tarceva
was approved by the FDA in November 2004(Johnson et al., 2005) and in the European
Union in September 2005 as monotherapy for the treatment of patients with locally advanced
or metastatic NSCLC after failure of at least one chemotherapy regimen. The evidence
suggests that Tarceva is more efficient than Iressa even though they were at the time
compared like Cola and Pepsi, almost completely similar. Today the difference is considered
to be mostly attributable to the fact that Tarceva was dosed at its maximum-tolerated dose
(MTD) (Shepherd et al., 2005), while Iressa was dosed at about one third of its MTD (Cohen
et al., 2004). Given the fact that EGF is an important growth promoting signaling pathway
that is expressed even in healthy cells, finding a growth inhibiting effect on a tumor upon
strong inhibition of EGFR seems logical. Although this dosage explanation is still debated we
consider this to be the primary reason for the varying results of both drugs. Due to this minor
difference in clinical strategy, Genentech was able to access the market without AstraZeneca
competing for a share of the profits for the following 5 years.
For both companies it has been fairly difficult to determine the right biomarker for their EGFR
TKI as the precise roles of EGFR and other components of this pathway have long been
debated (Dziadziuszko et al, 2006; Liang et al., 2010). Approximately 70% of NSCLCs with
EGFR mutations (exon 19 deletions or the exon 21 L858R) attain responses to EGFR Iressa
and Tarceva, with improved response rate (RR), progression-free survival (PFS) and in some
reports overall survival (OS) (Gaughan et al., 2011). The European Randomized Trial of
Tarceva vs. Chemotherapy (EURTAC) Phase III study was stopped early because it met its
primary endpoint, demonstrating that activating EGFR mutations were a suitable biomarker.
Today Tarceva has been approved in over 90 countries and used to treat more than a
quarter of a million patients (Roche year reports). Similarly, after a painstaking approval
process AstraZeneca was able to revive Iressa based on data from the Phase III INTEREST
study and the Phase III IPASS study, which compared Iressa with doublet chemotherapy
(carboplatin/paclitaxel) in 1st line NSCLC patients, a marketing authorization for Iressa for
the treatment of EGFR mutation positive advanced NSCLC patients (all lines of therapy) was
granted by the EMEA in June 2009, followed by the European launch in July (AstraZeneca
year reports). Interestingly, aside from targeting EGFR mutation positive patients the UK
National Institute of health and Clinical Excellence (NICE) has also demanded that Iressa
can only be prescribed at a fixed price in agreement with UK patient access schemes (NICE,
2010).
28 | P a g e
Figure 12 Yearly revenues of Iressa and Tarceva in Million US Dollars. In 2010 Iressa generates $311 million USD with
accumulated revenue of $2305 million USD. Tarceva generates $1325 million USD in 2010 with accumulated revenue of
$6156 million USD. Tarceva has thus generated $3851 million USD more than Iressa.
So, what if AstraZeneca had co-developed a biomarker for EGFR activating mutations and
would have been granted continued market approval in 2005 by the FDA? What would have
been the influence on its revenues compared to the current situation? Iressa would have
taken the market by storm, crushing Genentech’s attempt to market Tarceva without a
companion diagnostic. When we compare both companies year reports it becomes clear that
Iressa could have had a much better market position and higher revenues without the 2005
label change (Figure 121). Of course it is difficult to compare the marketing strategies of two
different companies and their drugs, but for this case study we see two possible scenarios
that can give us an indication on the added value of a biomarker for Iressa.

Situation A would be our primary assumption that Iressa takes the market by storm.
Positioning themselves as first on the market in 2003 would -in marketing terms- be
enough to built their brand and achieve at least a 2:1 market share compared to Tarceva
(Ries and Trout, 1981). Given that in this case Iressa has a biomarker and Tarceva
initially not, we further assume that Genentech would have a hard time getting Tarceva
approved at all, as the standard of care for Iressa would be much higher. So presumably
Tarceva would enter the market even later than it did now, leaving it safe to say that
Iressa could achieve revenues similar to Tarceva in the current situation, basically
switching revenues for both drugs.
29 | P a g e

Situation B would be a more conservative scenario in which Genentech quickly learns
from AstraZenecas success and develops a biomarker of its own for Tarceva. Their
market entry would be postponed even further, but given the fact that Tarceva has
performed better than Iressa in the past and assuming that part of Tarcevas success can
be attributed to better R&D and marketing strategies of Genentech, we estimate that in
the seven years since Iressa was launched both drugs reach a 50% market share. So as
Iressa enters the market ~2 years earlier the accumulated revenues for Iressa will still be
higher than those of Tarceva.
Table 2 Biomarker value for Iressa. Based on the revenues of Iressa and Tarceva in the current situation we estimate the
value of a biomarker for Iressa under the conditions described for Situation A and Situation B. In Situation A there is a
switch in revenues between Iressa and Tarceva, generating an accumulated biomarker value of $3851 million USD and
increasing 2010 revenues for Iressa to $1325 million USD. In situation B both drug reach a 50% market share plateau in
2010, generating an accumulated biomarker value of $2631 million USD and increasing 2010 revenues for Iressa to $818
million USD.
Thus, if AstraZeneca would have developed Iressa with an effective biomarker, this
biomarker should have been valued between $2631 and $3851 million USD in 2010 and
increase annual revenues by a staggering 263% - 426%. A good biomarker would probably
exclude a substantial number of patients that in the past did receive Iressa or Tarceva
without a positive effect on their disease. But as both drugs have a biomarker today and
physicians have been testing for EGFR status even before this was legally required we
assume this case to be an accurate estimation in demonstrating the added value of a
biomarker.
Learning points:

The initially failed attempt to market Iressa without a biomarker was very
damaging to AstraZenecas image and branding activities.

A biomarker for Iressa would have been valued $2.5 and $4 billion USD in 2010
and increase annual revenues by 250% - 425%.
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Comparing Iressa and Tarceva to Herceptin. A weighted pooled analysis of 60 available
studies has been done to evaluate the clinical outcome in patients with EGFR-mutated
NSCLC who were treated with chemotherapy or EGFR TKIs. In this analysis, the overall
median PFS was 13.2 months with Tarceva, 9.8 months with Iressa and 5.9 months with
chemotherapy (Paz-Ares et al., 2009). This illustrates that TKIs are still not performing as
well as one might expect for such novel targeted therapeutics. Especially when compared to
Herceptin, which is able to actually cure patients of their disease rather than prolonging their
PFS. So if we want to compare Herceptin to the EGFR TKIs we should to take into account
variables like differences in the disease or the total number of patients etc. For instance, in
contrast to HER2 in most breast cancers, EGFR expression in NSCLC is often
heterogeneous, varying between different regions within a single tumor and between primary
and metastatic tumors in the same patient (Eberhard et al., 2008)
Another major problem with
EGFR TKIs is that most tumors
initially respond but over time
(median of 6-12 months) most
tumors
develop
acquired
resistance.
Two
major
mechanisms of resistance have
been identified; T790M mutation
in
50%
of
EGFR-mutated
patients with TKI resistance and
the amplification of the MET
oncogene, present in 20% of TKIresistant tumors. In half of the
cases of the MET oncogene the
T790M is coexistent. It is
possible that other kinases (such
as insulin-like growth factor-1
receptor [IGF-1R]) might also be
selected to bypass EGFR
pathways in resistant tumors.
The growing preclinical data in
EGFR-mutated NSCLCs with
acquired resistance to Iressa or
Figure 13 Mechanisms of acquired resistance to EGFR TKIs in EGFR–mutated
NSCLC. Secondary resistance due to mutations in T790M and MET amplification.
Tarceva has spawned the initiation of clinical trials testing novel EGFR inhibitors that in vitro
inhibit T790M (Neratinib, XL647, BIBW 2992, and PF-00299804), MET, or IGF-1R inhibitors in
combination with EGFR TKIs, and heat shock protein 90 inhibitors (Nguyen et al., 2009). So if,
in the future, EGFR TKIs are administered earlier in the disease process, tumors will be less
genetically instable and as a whole contain less cell that might undergo mutation. Together
with a better understanding of the genetic background of acquired resistance it seems
plausible that Iressa and Tarceva become a lot more efficient and have successes that are
similar to Herceptin today.
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In 1998, the FDA approved Herceptin as one of the first targeted therapeutics. It is prescribed
only to patients whose genetic tests reveal an over-expression and amplification of the HER2
gene, which is highly amplified (two- to five-fold compared to normal) in 25% to 30% of
breast cancers. HER2 amplification along with receptor protein over-expression is a central
driving force in breast tumor growth and indicates more aggressive tumor behavior and poor
prognosis (Hicks et al., 2005). As a result of HER2 induced growth and progression, primary
tumor masses generally consist of a homogeneous pool of HER2-amplified cells and this
HER2+ status is maintained in tumor metastases (Carlsson et al., 2004; Paik et al., 1990)
Herceptin is a monoclonal antibody directed against the defective HER2 protein, effectively
taking out only tumor cells, whilst not inhibiting the growth of cells expressing normal levels of
HER2 (Mass et al., 2005). Breast cancer is the second leading cause of cancer deaths in
women today (after lung cancer) and is the most common cancer among women. According
to the World Health Organization, worldwide about 1.4 million women will be diagnosed with
breast cancer annually and about 458,000 will die from the disease.
In this case study we compare Tarceva to Herceptin according to a publication of Sophie
Kornowski-Bonnet, General Manager Roche Paris (Konowski-Bonnet, 2009) on a Roche
forum meeting. In France the primary variable in deciding which therapies are reimbursed is
the quality of the drug. France uses a rating system called the “Amelioration du Service
Medical Rendu” (ASMR) or evaluation of therapeutic benefit. This is expressed as a number
between 1 (top/best rating) and 5 (worst rating). So drugs with a better rating can demand a
higher price and will gain faster market access. This “quality of treatment” rating system can
be clearly seen in the reimbursement price of a product and its sales. Herceptin for example
has a 79 percent market share penetration in France, while Tarceva has a market share of
20-25 percent. The primary reason is that Herceptin uses diagnostics to stratify patient
populations, while Tarceva at the time did not have a proper biomarker. Tarceva sales show
a plateau and even decrease in year 5 after market entry because of shorter treatment
duration (in the majority, non-responder patients) and no deeper market penetration. In their
model Roche assumes a 60% smaller patient population, targeting only those patients most
likely to respond. Despite a smaller population, the modeled sales turnover is much higher
when Tarceva uses diagnostics. Longer treatment duration, coupled with high response in
the targeted population, leads to a higher ASMR rating and higher initial price for the drug.
This price doesn’t erode over time, because of the perceived high value of the drug by pricing
authorities (Figure 14).
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Figure 14 Tarceva modeled with a predictive test. Despite a smaller population, the modeled sales
turnover for Tarceva is much higher when using a biomarker.
The reverse is also interesting, without a stratifying test for Herceptin, Roche could offer
treatment to every breast cancer patient as an option. Although the patient population would
significantly increase, sales would decrease because treatment duration in the majority of
patients would be comparatively short. The ASMR rating would be lower, resulting in a lower
initial price. Market penetration would also shrink because physicians would not value
Herceptin as high as they do today. So without a biomarker, Herceptin would actually have
less real market value, as shown in Figure 15.
Figure 15 Herceptin modeled without its predictive test. Without a biomarker, Herceptin would have
much less real market value.
33 | P a g e
According to Roche’s hypothesis, adding a biomarker to Tarceva would generate a very large
value, as demonstrated in (Error! Reference source not found.). If we take these figures for
rance and extrapolate them to worldwide cancer incidence rates we see that the comparison
between Herceptin and Tarceva is not as farfetched as might seem. Sales of Tarceva indeed
reach a plateau in about five years after launch and the yearly revenues of Herceptin show
remarkable similarities to both tests with patient stratification in (Figure 14) and (Figure 15).
Figure 16 Yearly revenues Iressa, Tarceva and Herceptin
To be comprehensive in this comparison we need to verify the total number of patients
eligible for Herceptin treatment and compare this to the total number of lung cancer patients
that would be suited for EGFR TKI treatment. The number of patients that would likely
respond to treatment with Herceptin is approximately 350.000 per year. A lot more than the
200.000 patients I estimated to be suited for EGFR TKI treatment yearly, but still the
numbers are of a comparable proportion.
Worldwide yearly breast cancer incidence*
25% of patients suited for Herceptin per year
1.400.000
350.000
Worldwide yearly lung cancer incidence*
75% NSCLC
Asia: 360.000 with 30% EGFR+
Rest: 840.000 with 10% EGFR+
Total patients suited for EGFR TKI treatment per year.
1.600.000
1.200.000
108.000
84.000
200.000
*As indicated by the WHO for 2008.
34 | P a g e
So as we noted before EGFR TKIs have some very specific issues with tumor heterogeneity
and acquired resistance that need to be resolved in the future for the drugs to reach their
desired effect. But assuming these obstacles will be overcome and a very conclusive
biomarker (or set of biomarkers) is found for EGFR TKIs, we can state that Tarceva could
have reached peak annual sales similar to those of Herceptin, adjusted for patient subgroup
sizes of both drugs.
Figure 17 Yearly revenues for Iressa, Tarceva, Potential EGFR TKI and Herceptin. Potential yearly
revenues for EGFR TKIs as a function of Herceptin yearly revenues. Figures from before 2005 were
extrapolated as there was no real potential market at this point because Tarceva was not yet on the
market.
After estimating the potential yearly revenues for EGFR TKIs (Situation C), modeled to the
success of Herceptin, we can return to our initial calculation of the added value of a
biomarker for Iressa. This ‘back of an envelope’ calculation might not be very precise, but it
does give us an indication of the order of magnitude that potential revenues for targeted
therapeutics with an appropriate biomarker are in. For example, since its launch in 1998
Herceptin has yielded accumulated revenues of over $25 billion USD and achieved peak
annual sales of over $4 billion USD since 2007 (Roche annual reports).
35 | P a g e
Table 3 Potential biomarker value for Iressa. Situation C demonstrates a potential biomarker value of almost USD$9 billion
and an increase in annual revenues of 800%.
Thus, if AstraZeneca developed Iressa with a very conclusive biomarker (or set of
biomarkers), this biomarker would have been valued between $2631 and $8888 million USD
in 2010 and increase annual revenues by a staggering 263% - 806%.
Learning points:

The initially failed attempt to market Iressa without a biomarker was very
damaging to AstraZenecas image and branding activities.

A conclusive biomarker for Iressa would have been valued between $2.5 and $9
billion USD in 2010 and increase annual revenues by 250% - 800%.
3.5 Revenues generated by personalized medicine
Now that we have seen how personalized medicine can significantly reduce development costs it
makes sense to have a look at the revenue side of the equation. Pharmaceutical companies are
anxiously clinging to the ‘blockbuster business model’ claiming they need blockbuster drug revenues
to earn back their investments. In the past this model has been very successful, but following recent
developments in genomics, the idea that ‘one size fits all’ is no longer sustainable. Rather companies
should try to stratify patients and tailor drugs to meet their specific needs. This ‘nichebuster business
model’ has already proven to be very successful.
In the ‘Iressa, Tarceva, Herceptin’ case I calculated the potential value of a biomarker for the
targeted drug Iressa. Although a biomarker shrinks patient subgroups, the corresponding drug will be
able to obtain a larger market share and prove a higher value to all parties involved. A conclusive
biomarker for Iressa would have been valued between $2.5 and $9 billion USD in 2010 and increase
annual revenues by ~250% - 800%. However there are other aspects of tailoring medicine to the right
patients that have proven to be profitable for pharmaceutical companies and will continue to pose
financial incentives for the implementation of personalized medicine.
36 | P a g e
Gleevec, Imatinib Mesylate. In patients with Chronic Myeloid Leukemia (CML) an abnormal protein
continuously induces white blood cell production. The chimerical Bcr-Abl protein expressed by CML
cells has constitutive tyrosine kinase activity, which is essential for the pathogenesis of the disease.
Gleevec, an ATP-competitive selective inhibitor of Bcr-Abl, has unprecedented efficacy for the
treatment of CML. Most patients with early stage disease achieve durable complete hematological
and complete cytogenetic remissions, with minimal toxicity (Deininger et al., 2003). The first reason
for its success is the fact that Bcr-Abl is a truly tumor selective target that is absent in wild type cells.
Secondly, in CML, Bcr-Abl is a single oncogenic alteration that sustains the malignant phenotype, so
inhibition of this protein is sufficient to treat the disease (Blagosklonny et al., 2003). What makes the
case of Gleevec so interesting is the fact that it aims at a very small proportion of the patient
population, but is still able to generate very high revenues. First, because of its high response rate
among stratified patients and excellent curing capabilities for a serious disease like CML, Novartis can
justify premium pricing for this drug. Secondly, Gleevec is able to turn CML into a chronic disease,
almost completely restoring patients’ quality of life, but at the same time addicting them to the drug.
This life time ‘addiction’ has unprecedented effects on the annual sales figures for Gleevec which
have risen to $4.265 million USD in 2010 (Novartis year reports).
Another interesting aspect of Gleevec is the fact that Novartis has been able to market it for other
diseases as well. In February 2002, the FDA granted Fast-Track approval for the treatment of specific
patients with Kit-positive inoperable and/or metastatic GastroIntestinal Stromal Tumors (GIST). So
while initially targeted for CML, a thorough understanding of the underlying causes of the disease
has allow Novartis to market their product for other diseases. This principle to enlarge the patient
population for targeted therapeutics has been demonstrated throughout the industry.
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4. Discussion
Because our population is rapidly aging we can expect absolute cancer numbers to rise dramatically
in the coming years. New healthcare approaches are urgently needed to temper the spiraling costs of
cancer treatment and built sustainable healthcare business models. At current rates we might have
to face decisions on how much our society is actually willing to pay to improve cancer patients’
quality of life. Disappointing developments in the pharmaceutical sector show that improving
treatment standards is a painstaking and expensive undertaking. In fact, the biggest reductions in
cancer incidence rates are not the result of therapeutics, but of prevention and to some extent of
diagnostics. Proper use of food preservatives has tempered stomach cancer; colonoscopy and better
diets reduced colon and rectum cancer. Lung cancer, the highest cancer related cause of death is
mainly caused by smoking. So it is generally accepted that prevention is the most effective way to
fight cancer. But this does not mean that we can linger, first of all because prevention cannot
generate results quick enough to face our current challenges and secondly because there will always
be a significant proportion of cancer incidence that cannot be prevented. Personalized medicine is
particularly suited for the field of oncology as tumors show high rates of heterogeneity and
aggressiveness that require a great deal of stratification and targeted therapeutics for optimal
individual treatment. Oncologists, patients and payers are eager for new treatments that insure
better quality of life, even at premium prices. The promises of personalized medicine are evident, but
indeed one of the more uncertain aspects of personalized medicine is whether the anticipated
benefits will be realized at an acceptable cost. Proper quantification of the costs and benefits of
targeted therapeutics and their cost effectiveness are obviously of interest to all stakeholders. In this
article I have built a case for the financial viability of personalized medicine, but it has to be noted
that this sentiment is not universally shared (Leeder and Spielberg, 2009). Recently released analyses
by the Deloitte Center for Health Solutions suggest that the return on investmen (ROI) depends on
particular scenarios and is different among different stakeholders. In their most relevant scenario
they estimate the ROI for patients, payers (insurance companies), biotech/pharma and diagnostics
firms, after investing in the co-development of a drug and a diagnostic. Especially the payer doesn’t
fare particularly well, never reaching a positive ROI in this scenario and in other scenarios facing ROI
realization over a six-year period. But as Deloitte note themselves, this is due to a model limitation.
The framework fails to reflect other benefits important to the payer, such as providing coverage for
treatment that is safer and more efficacious, and for increasing sales to personalized medicine
candidates to increase throughput of patients and resulting benefits attributed to personalized
medicine (Deloitte, 2009). Also diagnostics companies seem to face a grim future by never reaching
their breakeven point. Every consumer who has the condition will receive a diagnostic test. This
represents significant sales revenues but is not sufficient to offset the large R&D expense to develop
the test. Diagnostic companies need higher pricing, lower costs or other strategies like government
funding or co-funding by pharmaceutical companies. However the model proposed by Deloitte is not
designed to adept modest changes in their assumptions and is in fact not at all compliant with some
of the far reaching implications of personalized medicine that we have proposed in this paper. So
although frequent reality checks are very favorable, I am of the opinion that cost effective business
models can be formed for personalized medicine and others share this opinion (Blair, 2009).
By lowering size, time and failure rates, especially of late phase clinical trials, development costs for
targeted therapeutics can be greatly reduced. Adaptive clinical trials will play a pivotal role in this
process. Justified by the communal benefits that PM has to offer, governments should put incentives
in place to further stimulate PM development. Incentives include tax benefits, alternative patent
protections like prolonged exclusivity, subsidizing and funding research and investing in grid services
and data exchanges to connect scientists and clinicians and increase data-mining capacities. Such
changes should ease development further and allow pharmaceutical and diagnostic companies to
38 | P a g e
develop new targeted therapeutics at affordable costs. My estimations reveal that the development
costs of a targeted therapeutic can be reduced by ~40% from $1799 million USD to $1065 million
USD if all the potential benefits for PM development can be achieved. At the same time revenues can
be sustained despite narrowing patient groups. In fact I believe that the development of a biomarker
could greatly increase annual revenues. Even the number of patients can be increased by opening
new markets for a drug as has been demonstrated by Gleevec. By reducing development times, drugs
will have a longer window of protection against generics, together with prolonging patients’ lives and
thus prolonged treatment periods so drug revenues can be greatly increased. Finally by proving to
supply an adequate, safe and cost-effective treatment option, companies can get premium prices for
their targeted therapeutics. In the case of Iressa a biomarker would have been valued between $2.5
and $9 billion USD in 2010 and increase annual revenues by a staggering 250% to 800%. These
findings built a strong case for pharmaceutical companies to explore the possibilities of personalized
medicine and start adopting measures to keep their businesses sustainable in the future.
In fact, some pharmaceutical companies are starting to explore the co-development of a biomarker
for their drugs. Pharmaceutical companies generally have two options for biomarker development;
combining diagnostics and therapeutics under one roof, or collaborating with external diagnostic
companies. The integrated approach might be the most promising as in-house diagnostics expertise
is likely to make it easier to involve diagnostics in the development process and it should benefit
most from any cost-savings or revenues increase throughout the entire process. Also, rather than
developing targeted therapeutics in their labs, most of the major pharmaceutical companies are
turning to small biotech firms to fill their pipeline. Roche is generally considered to be the leader in
terms of personalized medicine and diagnostics co-development. Not only does the company have
several drugs on the market that come with a companion diagnostic (Herceptin, Tarceva covered in
this article) it also has a PM policy in place since the beginning of 2009 that stipulates that every
single product in their pipeline should have an associated program to identify biomarkers.
Furthermore they have the advantage of having an in-house diagnostics unit. But the number of
mergers and acquisitions has greatly increased throughout the entire industry. Merck KGaA and
Novartis are now closely behind Roche. Merck has gained experience through Erditux (cetuximab)
and a high percentage of pipeline drugs with biomarker programs and Novartis has founded a
molecular diagnostics unit to facilitate biomarker research in its oncology franchise.
Despite the enormous potential for the industry and society, many challenges remain for
personalized medicine to become a mainstream medical practice. In addition to technical and
financial obstacles there are many practical challenges ahead as well. There are regulatory issues
surrounding targeted therapeutics and tests so companies still face uncertainty over the evidence
requirements. Other key challenges include needs related to information gathering, sharing and
interpretation, like standards in electronic medical records and dealing with the privacy and ethical
issues of DNA collection. Further, we need to develop new strategies to educate practitioners and
patients/consumers so they can make informed choices about a therapy. Understanding both the
benefits and limitations of personalized medicine will be of vital importance. Only if we can
overcome the obstacles discussed throughout this paper will we be able to fully realize the potential
of patient stratification and targeted therapeutics. The individualization of cancer treatment will
greatly improve prognosis for patients as treatments will prove more effective, saving vital time and
suffering. Personalized medicine has the power to revolutionize the practice of medicine and greatly
enhance treatment outcome for many individual patients. I truly believe the effects of such an
industry-wide change to be cost-effective and highly favorable to society.
39 | P a g e
References
Adams C., and Brantner V (2006) Estimating The Cost Of
New Drug Development: Is It Really $802 Million? Health
Affairs, 25, no. 2 (2006): 420-428
Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P
(2002), Molecular biology of the cell, fourth edition.
BCG (2001) A Revolution in R&D: How Genomics and
Genetics are Transforming the Biopharmaceutical
Industry. (Boston Consulting Group, Boston,
Massachusetts, 2001).
Berg D, Hipp S, Malinowsky K, Böllner C, Becker KF.
(2010) Molecular profiling of signalling pathways in
formalin-fixed and paraffin-embedded cancer tissues.
Eur J Cancer. 2010 Jan;46(1):47-55.
Bernards R.(2010) It's diagnostics, stupid. Cell. 2010 Apr
2;141(1):13-7.
Bhatt AN, Mathur R, Farooque A, Verma A, Dwarakanath
BS. (2010) Cancer biomarkers - current perspectives.
Indian J Med Res. 2010 Aug;132:129-49.
Blagosklonny MV, Darzynkiewicz Z. (2003) Why Iressa
failed: toward novel use of kinase inhibitors (outlook).
Cancer Biol Ther. 2003 Mar-Apr;2(2):137-40.
Blair (2010) Molecular diagnostics and personalized
medicine: value-assessed opportunities for multiple
stakeholders. Personalized Medicine (2010) 7(2), 143–
161.
The Biomarkers Consortium (2010) The Biomarkers
Consortium launches I-SPY 2 breast cancer clinical trial.
(Press release) 2010 Mar 17. Downloaded from:
http://ispy2.org/docs/ISPY2_Press%20Release_3-1510.pdf
Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto
P, Larkin J, Dummer R, Garbe C, Testori A, Maio M, Hogg
D, Lorigan P, Lebbe C, Jouary T, Schadendorf D, Ribas A,
O'Day SJ, Sosman JA, Kirkwood JM, Eggermont AM,
Dreno B, Nolop K, Li J, Nelson B, Hou J, Lee RJ, Flaherty
KT, McArthur GA; BRIM-3 Study Group. (2011) Improved
survival with vemurafenib in melanoma with BRAF
V600E mutation. N Engl J Med. 2011 Jun
30;364(26):2507-16. Epub 2011 Jun 5.
Chen E, Tong KB, Malin JL. (2010) Cost-effectiveness of
70-gene MammaPrint signature in node-negative breast
cancer. Am J Manag Care. 2010 Dec 1;16(12):e333-42.
Chow and Chang (2008), Adaptive design methods in
clinical trials – a review. Orphanet Journal of Rare
Diseases 2008, 3:11
CMS (2006) Guidance for the Public, Industry, and CMS
Staff: National Coverage Determinations with Data
Collection as a Condition of Coverage: Coverage with
Evidence Development. Document Issued on July 12,
2006.
Cohen MH, Williams GA, Sridhara R (2004) United States
Food and Drug Administration Drug Approval summary:
Gefitinib (ZD1839; Iressa) tablets. Clin Cancer Res
10:1212-1218, 2004.
CVZ (2010) Comparing Erlotinib and Gefitinib,
downloaded from:
http://www.cvz.nl/binaries/live/cvzinternet/hst_content
/nl/documenten/cfhrapporten/2010/cfh1001+gefitinib+iressa.pdf
Deininger MW, Druker BJ. (2003) Specific targeted
therapy of chronic myelogenous leukemia with imatinib.
Pharmacol Rev. 2003 Sep;55(3):401-23. Epub 2003 Jul
17.
Blair (2008) Assessing the Value-Adding Impact of
Diagnostic-Type Tests on Drug Development and
Marketing, Mol Diag Ther 2008; 12 (5): 331-337
DiMasi JA, Grabowski HG. (2007) Economics of new
oncology drug development. J Clin Oncol. 2007 Jan
10;25(2):209-16.
Booth B, Glassman R, Ma P.(2003) Oncology's trials. Nat
Rev Drug Discov. 2003 Aug;2(8):609-10.
Bowalekar (2011) Adaptive designs in clinical trials.
Perspect Clin Res. 2011 Jan;2(1):23-7.
Douglas, Mitchell (2008) ASSESSING RISK AND RETURN:
PERSONALIZED MEDICINE DEVELOPMENT & NEW
INNOVATION PARADIGM, Ewing Marion Kauffman
Foundation, 2008, Downloaded from:
http://www.kauffman.org/research-andpolicy/assessing-risk-and-return.aspx
Carlsson J, Nordgren H, Sjostrom J (2004) HER2
expression in breast cancer primary tumors and
corresponding metastases: Original data and literature
review, Br J Cancer 90:2344-2348, 2004.
Cataldo, Gibbons, Pérez-Soler, Quintás-Cardama, (2011)
Treatment of Non–Small-Cell Lung Cancer with Erlotinib
or Gefitinib. N Engl J Med 2011; 364:947-955 March 10.
Drolet M, Maunsell E, Mondor M, Brisson C, Brisson J,
Mâsse B, Deschênes L. (2005) Work absence after breast
cancer diagnosis: a population-based study., CMAJ. 2005
Sep 27;173(7):765-71.
Dziadziuszko R, Hirsch FR, Varella-Garcia M, Bunn PA Jr.
(2010) Selecting lung cancer patients for treatment with
epidermal growth factor receptor tyrosine kinase
40 | P a g e
inhibitors by immunohistochemistry and fluorescence in
situ hybridization--why, when, and how? Clin Cancer Res.
2006 Jul 15;12(14 Pt 2):4409s-4415s.
Gerges N, Rak J, Jabado N. (2010) New technologies for
the detection of circulating tumour cells. Br Med Bull.
2010;94:49-64. Epub 2010 Apr 23.
Eberhard, Giaccone, Johnson (2008) Biomarkers of
Response to Epidermal Growth Factor Receptor
Inhibitors in Non–Small-Cell Lung Cancer Working Group:
Standardization for Use in the Clinical Trial Setting,
Journal of Clinical Oncology, Vol 26, No 6 (February 20),
2008: pp. 983-994.
Hassett, MD, MPH ; O’Malley, PhD Keating, MD, MPH
(2009) Factors Influencing Changes in Employment
Among Women With Newly Diagnosed Breast Cancer,
Cancer 2009;115:2775–82.
EMEA (2006) Reflection paper on methodological issues
in confirmatory clinical trials with flexible design and
analysis plan. London, UK:. European Medicines Agency.
Report no: Doc. Ref. CHMP/EWP/2459/02.
EvaluatePharma (2009) Alpha World Preview 2014.
Downloaded from:
http://www.evaluatepharma.com/worldpreview2014.as
px
FDA (2006) Innovation or Stagnation: Critical Path
Opportunities List. Washington DC, USA: Food and Drug
Administration; 2006. Downloaded from:
http://www.fda.gov/downloads/ScienceResearch/Specia
lTopics/CriticalPathInitiative/CriticalPathOpportunitiesRe
ports/UCM077258.pdf
FDA (2010) Guidance for industry: Adaptive design
clinical trials for drugs and biologics. Washington DC,
USA: Food and Drug Administration; 2010. Downloaded
from:
http://www.fda.gov/downloads/Drugs/GuidanceCompli
anceRegulatoryInformation/Guidances/UCM201790.pdf
[cited on 2010 Apr 30]
FDA (2011) 2011 FDA Budget Summary, downloaded
from:
http://www.fda.gov/AboutFDA/ReportsManualsForms/R
eports/BudgetReports/2011/default.htm
Fukuoka M, Yano S, Giaccone G (2003) Multi-institutional
randomized phase II trial of gefitinib for previously
treated patients with advanced non-small cell lung
cancer. J Clin Oncol 21:2237-2246, 2003.
Gagnon MA, Lexchin J (2008) The cost of pushing pills: a
new estimate of pharmaceutical promotion
expenditures in the United States, PLoS Med. 2008 Jan
3;5(1):e1.
Gahan (2010) Circulating nucleic acids in plasma and
serum: diagnosis and prognosis in cancer, EPMA Journal,
2010, 1:503–512
Gaughan EM, Costa DB.(2011) Genotype-driven
therapies for non-small cell lung cancer: focus on EGFR,
KRAS and ALK gene abnormalities, Ther Adv Med Oncol.
2011 May;3(3):113-25.
Haddow JE, Palomaki GE. (2003) ACCE: A Model Process
for Evaluating Data on Emerging Genetic Tests. In:
Human Genome Epidemiology: A Scientific Foundation
for Using Genetic Information to Improve Health and
Prevent Disease. Oxford University Press, pp. 217-233,
2003.
Hansen HH. (2002) Treatment of advanced non-small cell
lung cancer, BMJ 2002, 325:452-453.
Hicks DG, Tubbs RR (2005) Assessment of the HER2
status in breast cancer by fluorescence in situ
hybridization: A technical review with interpretive
guidelines. Hum Pathol 36:250-261, 2005.
Johnson JR, Cohen M, Sridhara R (2005) Approval
summary for Erlotinib for treatment of patients with
locally advanced or metastatic non-small cell lung cancer
after failure of at least one prior chemotherapy regimen,
Clin Cancer Res 11:6414-6421, 2005
Jones D. (2010) Adaptive trials receive boost. Nat Rev
Drug Discov. 2010;9:345–8.
Keckley PH, Dhar A, Underwood HR. (2009) The ROI for
targeted therapies: a strategic perspective. Washington,
DC: Deloitte Center for Health Solutions, 2009.
Downloaded from:
www.deloitte.com/dtt/cda/doc/content/us_chs_ROIforT
argeted Therapies_January2009%281%29.pdf
Kola I, Landis J (2004) Can the pharmaceutical industry
reduce attrition rates? Nat Rev Drug Discov. 2004
Aug;3(8):711-5.
Kornowski-Bonnet, Discussing the (as yet) untested
business case behind PHC (2009) Downloaded from:
http://www.roche.com.pk/media030809a.html
Kris MG, Natale RB, Herbst RS (2003) Efficacy of gefitinib,
an inhibitor of the epidermal growth factor receptor
tyrosine kinase, in symptomatic patients with non-small
cell lung cancer. JAMA 290:2149-2158, 2003.
Lau, Dry, Harbron, Knights, Riches, Mangena, Avis,
Brown, Runswick, Dearden, O'Shaughnessy and
O'Connor (2009) Identification of gene expression
biomarkers that predict sensitivity to the PARP inhibitor
olaparib . Mol Cancer Ther 2009;8(12 Suppl):C119.
Lauzier S, Maunsell E, Drolet M, Coyle D, Hébert-Croteau
N, Brisson J, Mâsse B, Abdous B, Robidoux A, Robert J.
(2008) Wage losses in the year after breast cancer:
41 | P a g e
extent and determinants among Canadian women. J Natl
Cancer Inst. 2008 Mar 5;100(5):321-32.
Mammaprint: from development to the MINDACT Trial.
Cancer Genomics Proteomics. 2007 May-Jun;4(3):147-55.
Leeder JS, Spielberg SP (2009)
Personalized medicine: reality and reality checks. Ann
Pharmacother. 2009 May;43(5):963-6.
Morgan S. Grootendorst P., Lexchine J., Cunninghama C.
(2011) The cost of drug development: A systematic
review. Health Policy 100 (2011) 4–17.
Liang Z, Zhang J, Zeng X, Gao J, Wu S, Liu T (2010)
Relationship between EGFR expression, copy number
and mutation in lung adenocarcinomas. BMC Cancer.
2010 Jul 19;10:376.
Müller V, Alix-Panabières C, Pantel K. (2010) Insights into
minimal residual disease in cancer patients: implications
for anti-cancer therapies. Eur J Cancer. 2010
May;46(7):1189-97. Epub 2010 Mar 27
De Luca A, Normanno N. (2010) Predictive biomarkers to
tyrosine kinase inhibitors for the epidermal growth
factor receptor in non-small-cell lung cancer. Curr Drug
Targets. 2010 Jul;11(7):851-64.
Nguyen KS, Kobayashi S, Costa DB. (2009) Acquired
resistance to epidermal growth factor receptor tyrosine
kinase inhibitors in non-small-cell lung cancers
dependent on the epidermal growth factor receptor
pathway. Clin Lung Cancer. 2009 Jul;10(4):281-9.
Ludovini V, Bianconi F, Pistola L, Chiari R, Minotti V,
Colella R, Giuffrida D, Tofanetti FR, Siggillino A, Flacco A,
Baldelli E, Iacono D, Mameli MG, Cavaliere A, Crinò L
(2012) Phosphoinositide-3-Kinase Catalytic Alpha and
KRAS Mutations are Important Predictors of Resistance
to Therapy with Epidermal Growth Factor Receptor
Tyrosine Kinase Inhibitors in Patients with Advanced
Non-small Cell Lung Cancer. J Thorac Oncol. 2011 Feb 12.
Lynda D Ralton, Graeme I Murray (2011) The use of
formalin fixed wax embedded tissue for proteomic
analysis, J Clin Pathol 2011;64:297-302
doi:10.1136/jcp.2010.086835
Mahajan and Gupta (2010) Adaptive design clinical trials:
Methodology, challenges and prospect. Indian J
Pharmacol. 2010 August; 42(4): 201–207.
Majewski IJ, Bernards R. (2011) Taming the dragon:
genomic biomarkers to individualize the treatment of
cancer. Nat Med. 2011 Mar;17(3):304-12.
Massarelli E, Varella-Garcia M, Tang X, Xavier AC, Ozburn
NC, Liu DD, Bekele BN, Herbst RS, Wistuba II (2007) KRAS
mutation is an important predictor of resistance to
therapy with epidermal growth factor receptor tyrosine
kinase inhibitors in non-small-cell lung cancer. Clin
Cancer Res. 2007 May 15;13(10):2890-6.
Mass RD, Press MF, Anderson S (2005) Evaluation of
clinical outcomes according to HER2 detection by
fluorescence in situ hybridization in women with
metastatic breast cancer treated with Trastuzumab, Clin
Breast Cancer 6:240-246, 2005.
Mead R, Duku M, Bhandari P, Cree IA. (2011) Circulating
tumour markers can define patients with normal colons,
benign polyps, and cancers. Br J Cancer. 2011 Jun 28.
doi: 10.1038/bjc.2011.230.
Mitsudomi T, Kosaka T, Yatabe Y. (2006) Biological and
clinical implications of EGFR mutations in lung cancer, Int
J Clin Oncol 2006;11:190–8
Mook S, Van't Veer LJ, Rutgers EJ, Piccart-Gebhart MJ,
Cardoso F. (2007) Individualization of therapy using
NICE (2010) Gefitinib for the first-line treatment of
locally advanced or metastatic non-small-cell lung
cancer, Downloaded from:
http://www.nice.org.uk/nicemedia/live/13058/49880/4
9880.pdf
Nirmalan NJ, Harnden P, Selby PJ, Banks RE. (2008)
Mining the archival formalin-fixed paraffin-embedded
tissue proteome: opportunities and challenges. Mol
Biosyst. 2008 Jul;4(7):712-20.
O'Shaughnessy J, Osborne C, Pippen JE, Yoffe M, Patt D,
Rocha C, Koo IC, Sherman BM, Bradley C. (2011) Iniparib
plus chemotherapy in metastatic triple-negative breast
cancer. N Engl J Med. 2011 Jan 20;364(3):205-14. Epub
2011 Jan 5.
Paik S, Hazan R, Fisher ER (1990) Pathologic findings
from the National Surgical Adjuvant Breast and Bowel
Project: Prognostic significance of erbB-2 protein over
expression in primary breast cancer, J Clin Oncol 8:103112, 1990.
Parker (2010) The Adverse Impact of the US
Reimbursement System on the Development and
Adoption of Personalized Medicine Diagnostics.
Paul SM, Mytelka DS, Dunwiddie CT, Persinger CC,
Munos BH, Lindborg SR, Schacht AL.(2010) How to
improve R&D productivity: the pharmaceutical industry's
grand challenge. Nat Rev Drug Discov. 2010
Mar;9(3):203-14. Epub 2010 Feb 19.
Paz-Ares L, Soulières D, Melezínek I, Moecks J, Keil L,
Mok T, Rosell R, Klughammer B. (2010) Clinical outcomes
in non-small-cell lung cancer patients with EGFR
mutations: pooled analysis. J Cell Mol Med. 2010
Jan;14(1-2):51-69. Epub 2009 Dec 8.
PWC (2008) The changing dynamics of pharma
outsourcing in Asia: are you readjusting your sights?,
PricewaterhouseCoopers. 2008, Downloaded from:
http://www.pwc.com/gx/en/pharma-lifesciences/outsourcing/index.jhtml
42 | P a g e
Quinlana, Gaydosb, Macac and Kramsd (2010) Barriers
and opportunities for implementation of adaptive
designs in pharmaceutical product Development, Clinical
Trials 2010; 7: 167–173
Reichert JM, Wenger JB. (2008) Development trends for
new cancer therapeutics and vaccines. Drug Discov
Today 2008;13:30 –37.
Ries A. and Trout J. (1981) Positioning: The Battle for
Your Mind, McGraw-Hill, 1981
Rosell R, Moran T, Queralt C (2009) Screening for
epidermal growth factor receptor mutations in lung
cancer, N Engl J Med 2009;361:958–67.
Schmid K, Oehl N, Wrba F, Pirker R, Pirker C, Filipits M
(2009) EGFR/KRAS/BRAF mutations in primary lung
adenocarcinomas and corresponding locoregional lymph
node metastases. Clin Cancer Res. 2009 Jul
15;15(14):4554-60.
Sharma SV, Settleman J (2010) Exploiting the balance
between life and death: targeted cancer therapy and
"oncogenic shock". Biochem Pharmacol. 2010 Sep
1;80(5):666-73.
Shepherd FA, Pereira JR, Ciuleanu T (2005) Erlotinib in
previously treated non-small-cell lung cancer, N Engl J
Med 353:123-132, 2005.
Sunaga N, Shames DS, Girard L, Peyton M, Larsen JE,
Imai H, Soh J, Sato M, Yanagitani N, Kaira K, Xie Y, Gazdar
AF, Mori M, Minna JD (2011) Knockdown of oncogenic
KRAS in non-small cell lung cancers suppresses tumor
growth and sensitizes tumor cells to targeted therapy.
Mol Cancer Ther. 2011 Feb;10(2):336-46.
Thatcher N, Chang A, Parikh P (2005) Results of a phase
III placebo-controlled study (ISEL) of gefitinib (IRESSA)
plus best supportive care (BSC) in patients with
advanced non-small-cell lung cancer (NSCLC) who had
received 1 or 2 prior chemotherapy regimens. Proc Am
Assoc Cancer Res 2005; Late Breaking Session:LB-6.
Tonon G. (2008) From oncogene to network addiction:
the new frontier of cancer genomics and therapeutics.
Future Oncol. 2008 Aug;4(4):569-77.
Van 't Veer LJ, Dai H, van de Vijver MJ, He YD, Hart AA,
Mao M, Peterse HL, van der Kooy K, Marton MJ,
Witteveen AT, Schreiber GJ, Kerkhoven RM, Roberts C,
Linsley PS, Bernards R, Friend SH., (2002), Gene
expression profiling predicts clinical outcome of breast
cancer. Nature. 2002 Jan 31;415(6871):530-6.
Weinstein IB, Joe A.(2008) Oncogene addiction. Cancer
Res. 2008 May 1;68(9):3077-80; discussion 3080.
WHO (2008) World Health Organization cancer statistics
by Globocan, Downloaded from:
http://globocan.iarc.fr/factsheets/cancers/lung.asp
http://globocan.iarc.fr/factsheets/cancers/breast.asp
Woodcock J. (2005) FDA introductory comments: clinical
studies design and evaluation issues. Clinical Trials 2005;
2: 273-275.
43 | P a g e
Glossary
New molecular entity (NME). A medication containing an active ingredient that has not been
previously approved for marketing in any form in the United States. NME is conventionally used to
refer only to small-molecule drugs, but in this article we use the term as a shorthand to refer to both
new chemical entities and new biologic entities.
Capitalized cost This is the out-of-pocket cost corrected for cost of capital, and is the standard
accounting treatment for long-term investments. It recognizes the fact that investors require a return
on research investments that reflects alternative potential uses of their investment. So, the
capitalized cost per drug launch increases out-of-pocket costs by the cost of capital for every year
from expenditure to launch.
Out-of-pocket cost This is the total cost required to expect one drug launch, taking into account
attrition, but not the cost of capital.
Cost of capital This is the annual rate of return expected by investors based on the level of risk of the
investment.
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Appendices
Appendix 1 Estimated costs of drug development. Modified from Morgan, 2011
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Appendix 2 “Dear Doctor letter” by AstraZeneca. Sent to practitioners upon market withdrawal of Iressa.
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